{"meta":{"query_hash":"a98249d5dc37","filters":{"venue":"BioMedInformatics"},"cohort_total":33,"direct_labels_cover":0,"predictions_cover":33,"exported":33,"export_cap":100000,"truncated":false,"label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"permalink":"https://metacan.xera.ac/q/a98249d5dc37","api":"https://metacan.xera.ac/api/v1/cohort?venue=BioMedInformatics"},"results":[{"id":"W4205128884","doi":"10.3390/biomedinformatics2010009","title":"State-of-the-Art Explainability Methods with Focus on Visual Analytics Showcased by Glioma Classification","year":2022,"lang":"en","type":"article","venue":"BioMedInformatics","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Austrian Science Fund","keywords":"Python (programming language); Interpretability; Computer science; Oligodendroglioma; Documentation; Glioma; Glioblastoma; Artificial intelligence; Analytics; Machine learning; Natural language processing; Astrocytoma; Data science; Programming language; Medicine","score_opus":0.012790368889791659,"score_gpt":0.3127530253599188,"score_spread":0.2999626564701271,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205128884","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011920598,0.0015748926,0.9132056,0.00094616774,0.000087906315,0.00019012553,0.0045319702,0.059510853,0.008031918],"genre_scores_gemma":[0.20072202,0.0031833504,0.7603043,0.0006390596,0.00017126922,0.0006205981,0.01812609,0.009135156,0.007098198],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99802125,0.00060466427,0.00016017012,0.00053538417,0.0005587268,0.000119832446],"domain_scores_gemma":[0.9928139,0.004745623,0.00040244363,0.0011924215,0.0006971261,0.00014854758],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032775623,0.0023132337,0.00061934267,0.0037473782,0.0005892085,0.0048122504,0.002158043,0.0010293993,0.01680902],"category_scores_gemma":[0.018360896,0.0006277326,0.0028304067,0.002488069,0.001017932,0.0036307892,0.0025265012,0.0019561846,0.004501163],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006096715,0.0003015719,0.010821531,0.0033282728,0.0005188104,0.0006363664,0.0019294886,0.045780092,0.011374819,0.061709963,0.050686784,0.8123026],"study_design_scores_gemma":[0.00018661172,0.00020243032,0.009324928,0.0012457826,0.0003923189,0.0008869773,0.0008744644,0.49690983,0.04767025,0.23010547,0.2119338,0.00026715707],"about_ca_topic_score_codex":0.0036183766,"about_ca_topic_score_gemma":0.0044671413,"teacher_disagreement_score":0.01680902,"about_ca_system_score_codex":0.0012777159,"about_ca_system_score_gemma":0.0015381553,"threshold_uncertainty_score":0.056231737},"labels":[],"label_agreement":null},{"id":"W4220741143","doi":"10.3390/biomedinformatics2010013","title":"Unobtrusive Monitoring of Sleep Cycles: A Technical Review","year":2022,"lang":"en","type":"review","venue":"BioMedInformatics","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Sleep (system call); Polysomnography; BitTorrent tracker; Smartwatch; Wearable computer; Scope (computer science); Wearable technology; Actigraphy; Computer science; Tracking (education); Sleep medicine; Applied psychology; Medicine; Psychology; Eye tracking; Sleep disorder; Artificial intelligence; Psychiatry","score_opus":0.051490784285742754,"score_gpt":0.3135364707335022,"score_spread":0.26204568644775944,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220741143","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00017786637,0.99860734,0.00044850516,0.00015960772,0.00013041109,0.00003687943,0.00004468851,0.000010824813,0.00038384547],"genre_scores_gemma":[0.0010062079,0.9977095,0.00073578156,0.00015761647,0.00013057682,0.000046931913,0.000069289,0.00000582737,0.0001382138],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9976005,0.0004244065,0.0007547382,0.00034848705,0.00076946197,0.00010253282],"domain_scores_gemma":[0.98949975,0.0069631846,0.0011010694,0.0002566307,0.0020213367,0.00015803112],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0044726157,0.0013123719,0.00238497,0.008142134,0.000490896,0.0019614084,0.0021142864,0.0017967393,0.0033115852],"category_scores_gemma":[0.010312246,0.0007233408,0.002552467,0.0056145256,0.0009189231,0.0032641757,0.001373087,0.001350879,0.0017135841],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013287287,0.000094909075,0.00072427536,0.15649104,0.0004257182,0.0002223051,0.00022494893,0.00027361995,0.0013986331,0.0014451927,0.008751801,0.82981473],"study_design_scores_gemma":[0.000056572168,0.0007991026,0.0070010205,0.17405415,0.0046708826,0.0040654596,0.00049575616,0.00060722,0.00276012,0.0026852565,0.8026309,0.00017357801],"about_ca_topic_score_codex":0.0025339033,"about_ca_topic_score_gemma":0.0026153093,"teacher_disagreement_score":0.008142134,"about_ca_system_score_codex":0.0008482408,"about_ca_system_score_gemma":0.0036729996,"threshold_uncertainty_score":0.023653746},"labels":[],"label_agreement":null},{"id":"W4225674000","doi":"10.3390/biomedinformatics1030013","title":"Gibbs Free Energy, a Thermodynamic Measure of Protein–Protein Interactions, Correlates with Neurologic Disability","year":2021,"lang":"en","type":"article","venue":"BioMedInformatics","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Gibbs free energy; Neuropathology; Disease; Correlation; Multiple sclerosis; Psychology; Neuroscience; Medicine; Psychiatry; Thermodynamics; Physics; Internal medicine; Mathematics","score_opus":0.006243973989118277,"score_gpt":0.19963915580801145,"score_spread":0.19339518181889317,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225674000","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9769992,0.0013707961,0.017052017,0.00031497038,0.000023450839,0.000023287612,0.0012511862,0.00013240206,0.0028327524],"genre_scores_gemma":[0.997825,0.00023429941,0.0012140742,0.000023352743,0.000009738735,0.00001514043,0.00038719526,0.000007839844,0.000283315],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9995789,0.00011325792,0.00003365495,0.00010007385,0.00012419508,0.000050036902],"domain_scores_gemma":[0.9955,0.0021917503,0.0014867155,0.00029126264,0.00028922182,0.0002410207],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007545142,0.00038264797,0.00039369473,0.002036271,0.00025530224,0.00058892614,0.00027745767,0.00039450548,0.0012606243],"category_scores_gemma":[0.0053528845,0.00015331864,0.00028452525,0.0015202377,0.00080609974,0.0006651623,0.00063649385,0.00049172796,0.00025987456],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043187488,0.0002453623,0.8648058,0.00033249144,0.0008883226,0.00046733476,0.00058502704,0.03940975,0.03376904,0.007219781,0.0013644889,0.050480653],"study_design_scores_gemma":[0.000013568391,0.00019814711,0.9066354,0.000038745016,0.000104363426,0.0009891645,0.0003168983,0.044750914,0.008381195,0.036139093,0.0023541246,0.00007842638],"about_ca_topic_score_codex":0.0016161989,"about_ca_topic_score_gemma":0.002445638,"teacher_disagreement_score":0.002036271,"about_ca_system_score_codex":0.0004417662,"about_ca_system_score_gemma":0.00028408886,"threshold_uncertainty_score":0.004217267},"labels":[],"label_agreement":null},{"id":"W4307238508","doi":"10.3390/biomedinformatics2040035","title":"Omicron SARS-CoV-2 Spike-1 Protein’s Decreased Binding Affinity to α7nAChr: Implications for Autonomic Dysregulation of the Parasympathetic Nervous System and the Cholinergic Anti-Inflammatory Pathway—An In Silico Analysis","year":2022,"lang":"en","type":"article","venue":"BioMedInformatics","topic":"SARS-CoV-2 and COVID-19 Research","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Cholinergic; In silico; Biology; Docking (animal); Protein–protein interaction; Neuroscience; Cell biology; Genetics; Medicine; Gene","score_opus":0.030885188216286823,"score_gpt":0.3085721851082739,"score_spread":0.27768699689198706,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4307238508","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9953904,0.00024471665,0.0017895498,0.000120432116,0.000019239169,0.00001979046,0.00074738683,0.00010031474,0.0015682208],"genre_scores_gemma":[0.9947843,0.00023805666,0.0023652588,0.00007139432,0.000006899884,0.000029386334,0.0019457035,0.000044456032,0.0005145006],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998357,0.000029178957,0.000010912327,0.000029017534,0.000045814893,0.00004936953],"domain_scores_gemma":[0.9997936,0.00008849024,0.000039332146,0.0000102504955,0.00003301984,0.00003531994],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027743517,0.00097220804,0.0009384016,0.00035013704,0.0004534532,0.00056221936,0.0006590177,0.00074576633,0.0024174051],"category_scores_gemma":[0.00062985043,0.00028758304,0.0013296688,0.0003280043,0.0002850569,0.00043972366,0.00035600257,0.00052015693,0.00036874553],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016293925,0.0007582102,0.05849484,0.0008208365,0.0006897419,0.0022055034,0.0001717796,0.77953196,0.14014986,0.0039967257,0.002882618,0.008668514],"study_design_scores_gemma":[0.0001767764,0.0008012342,0.01744061,0.00004222133,0.000253385,0.00039369756,0.00018913628,0.964873,0.013146132,0.0011479094,0.0014912246,0.000044596185],"about_ca_topic_score_codex":0.008396695,"about_ca_topic_score_gemma":0.007491862,"teacher_disagreement_score":0.008396695,"about_ca_system_score_codex":0.0004985891,"about_ca_system_score_gemma":0.0006821875,"threshold_uncertainty_score":0.016695619},"labels":[],"label_agreement":null},{"id":"W4321092570","doi":"10.3390/biomedinformatics3010010","title":"A Genome-Wide Association Study of Dementia Using the Electronic Medical Record","year":2023,"lang":"en","type":"article","venue":"BioMedInformatics","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan; University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada; China Scholarship Council; Canada Research Chairs; Michael Smith Health Research BC; Western Canada Research Grid; Compute Canada","keywords":"Genome-wide association study; Single-nucleotide polymorphism; False positive paradox; Dementia; Multiple comparisons problem; Genetic association; False discovery rate; SNP; False positive rate; Computational biology; Genome; False positives and false negatives; Genetics; Biology; Gene; Computer science; Medicine; Artificial intelligence; Disease; Mathematics; Statistics; Genotype","score_opus":0.018093136443999673,"score_gpt":0.2863418853623382,"score_spread":0.2682487489183385,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321092570","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97209936,0.0059800916,0.00627882,0.0008621065,0.00013908432,0.000079450816,0.011005435,0.000075839474,0.0034798484],"genre_scores_gemma":[0.98607427,0.001381532,0.005423871,0.00028565238,0.00007835874,0.0000792964,0.0058468743,0.000010666716,0.0008193914],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99574846,0.0019853136,0.00056108605,0.00086833275,0.0006314776,0.00020527538],"domain_scores_gemma":[0.9945332,0.0027143294,0.0014691736,0.00070900697,0.00040642646,0.00016786481],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028809358,0.0002748637,0.00052946963,0.0020972267,0.0005859494,0.0009430867,0.00040350482,0.0007161727,0.0022845948],"category_scores_gemma":[0.009205934,0.00015994959,0.0007169402,0.005157903,0.00023695923,0.0005590168,0.0004991526,0.000560476,0.0003919807],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031686426,0.0001014379,0.97964066,0.00025289474,0.0013021568,0.0003912201,0.0001282003,0.00010903506,0.001706094,0.00027703654,0.00076190755,0.01501249],"study_design_scores_gemma":[0.00003007811,0.0001683403,0.9939926,0.000058065383,0.0006843739,0.0008511616,0.000099643825,0.0004090703,0.0007491598,0.00024682295,0.0026973675,0.000013245915],"about_ca_topic_score_codex":0.0038317733,"about_ca_topic_score_gemma":0.0077097267,"teacher_disagreement_score":0.0038317733,"about_ca_system_score_codex":0.00025257244,"about_ca_system_score_gemma":0.0005066784,"threshold_uncertainty_score":0.01523602},"labels":[],"label_agreement":null},{"id":"W4372286023","doi":"10.3390/biomedinformatics3020023","title":"An Efficient COVID-19 Mortality Risk Prediction Model Using Deep Synthetic Minority Oversampling Technique and Convolution Neural Networks","year":2023,"lang":"en","type":"article","venue":"BioMedInformatics","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"","keywords":"Artificial intelligence; Oversampling; Deep learning; Computer science; Convolutional neural network; Machine learning; Autoencoder; Artificial neural network; Telecommunications","score_opus":0.059919602785599595,"score_gpt":0.35150873537243865,"score_spread":0.29158913258683905,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4372286023","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.36243737,0.0021608933,0.62517124,0.0016949492,0.00033682046,0.00011852976,0.00123088,0.002235894,0.0046134433],"genre_scores_gemma":[0.9610995,0.00046327017,0.032265946,0.00022055156,0.000089960144,0.00009533499,0.0014734621,0.000040936953,0.0042510564],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998313,0.000023068973,0.000013474864,0.000052328156,0.000039911418,0.00003989232],"domain_scores_gemma":[0.99975973,0.000077696386,0.000030271092,0.000012187336,0.00009991943,0.000020206206],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064112665,0.0007497475,0.00074162846,0.00057178555,0.00030586318,0.0005935542,0.0011814788,0.00070069847,0.001019112],"category_scores_gemma":[0.001008123,0.00031826828,0.00069689384,0.0003446279,0.00019504919,0.0005498365,0.0005237919,0.0009899325,0.00027839013],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027359984,0.00016674369,0.011024838,0.00005693857,0.00009200131,0.00021221607,0.00004961778,0.8682992,0.0033866751,0.0017561858,0.0034543986,0.11122756],"study_design_scores_gemma":[0.0000016783985,0.000005601912,0.00021047439,0.0000014864186,0.0000036286406,0.000006390212,0.0000014266978,0.9994222,0.00016316494,0.000119087235,0.000063026804,0.0000017944958],"about_ca_topic_score_codex":0.03024304,"about_ca_topic_score_gemma":0.022490397,"teacher_disagreement_score":0.03024304,"about_ca_system_score_codex":0.000886029,"about_ca_system_score_gemma":0.0011202764,"threshold_uncertainty_score":0.060133994},"labels":[],"label_agreement":null},{"id":"W4386369761","doi":"10.3390/biomedinformatics3030045","title":"Deep Learning and Federated Learning for Screening COVID-19: A Review","year":2023,"lang":"en","type":"review","venue":"BioMedInformatics","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Deep learning; Artificial intelligence; Computer science; 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Machine learning; Medical physics; Data science; Computed tomography; Medicine; Radiology; Disease; Infectious disease (medical specialty); Pathology","score_opus":0.1447354331048505,"score_gpt":0.43841388447309654,"score_spread":0.293678451368246,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386369761","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0002496373,0.99790525,0.0008378365,0.00044873302,0.00007243129,0.000017060489,0.00006994433,0.000011512086,0.00038761343],"genre_scores_gemma":[0.004549086,0.9926293,0.0019072315,0.0004554673,0.00012436972,0.000043205822,0.000132029,0.000005038722,0.0001542978],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99886096,0.00045584634,0.00021774201,0.00017833638,0.00023591892,0.000051221763],"domain_scores_gemma":[0.99426794,0.004835871,0.00030292192,0.00009036453,0.00043782886,0.00006497314],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036585426,0.0010751714,0.001893114,0.0029978715,0.0002596779,0.0012627399,0.0012334901,0.0012067575,0.0028007352],"category_scores_gemma":[0.011719898,0.00039527076,0.0028402482,0.0028522494,0.0003833433,0.0012744052,0.0008099735,0.0012255873,0.0006225265],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017416001,0.000077686294,0.0010739471,0.0729655,0.0019231869,0.000056856778,0.000058322337,0.002133552,0.00020963399,0.0028883356,0.008812221,0.90962666],"study_design_scores_gemma":[0.0003531152,0.0018705839,0.010214536,0.23397899,0.022850279,0.001801852,0.00037169884,0.010609816,0.002719748,0.02153073,0.6934472,0.0002515635],"about_ca_topic_score_codex":0.0038902154,"about_ca_topic_score_gemma":0.005120587,"teacher_disagreement_score":0.0038902154,"about_ca_system_score_codex":0.0010464257,"about_ca_system_score_gemma":0.0030601867,"threshold_uncertainty_score":0.019348502},"labels":[],"label_agreement":null},{"id":"W4387461511","doi":"10.3390/biomedinformatics3040052","title":"Weighted Trajectory Analysis and Application to Clinical Outcome Assessment","year":2023,"lang":"en","type":"article","venue":"BioMedInformatics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; University of Alberta","funders":"","keywords":"Estimator; Statistics; Sample size determination; Computer science; Medicine; Mathematics","score_opus":0.1479931251245986,"score_gpt":0.5004252451851979,"score_spread":0.3524321200605993,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387461511","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00870511,0.00034876735,0.9892266,0.00024243249,0.000043582553,0.00015465451,0.00030786215,0.00033186277,0.000639028],"genre_scores_gemma":[0.30619684,0.00097560935,0.68812495,0.00022929402,0.000091983144,0.0012279295,0.0010345505,0.00027217486,0.0018467013],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99510264,0.0033335192,0.00031889742,0.00047917027,0.0006531629,0.000112575064],"domain_scores_gemma":[0.9848969,0.01116903,0.0015309927,0.00092235015,0.001234577,0.00024622967],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009936846,0.00080973003,0.0009678666,0.0022775657,0.00037933013,0.0011840549,0.001022884,0.00085276854,0.003543975],"category_scores_gemma":[0.044118576,0.00034865862,0.0014377915,0.0019289026,0.0006925681,0.0011063552,0.0016377891,0.0016806537,0.000630762],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00046691336,0.00014706954,0.01918186,0.0005649291,0.00070610485,0.00043018875,0.0005612378,0.4557837,0.0030544964,0.10498826,0.00459313,0.40952206],"study_design_scores_gemma":[0.000043726526,0.00016270083,0.0027576766,0.000078746496,0.00005838452,0.0001666046,0.00006703669,0.9041463,0.0009372167,0.08662503,0.0049165306,0.00004004968],"about_ca_topic_score_codex":0.0051531754,"about_ca_topic_score_gemma":0.0028825786,"teacher_disagreement_score":0.009936846,"about_ca_system_score_codex":0.0010216059,"about_ca_system_score_gemma":0.0019418214,"threshold_uncertainty_score":0.052551687},"labels":[],"label_agreement":null},{"id":"W4388126035","doi":"10.3390/biomedinformatics3040058","title":"Federated Learning for Diabetic Retinopathy Detection Using Vision Transformers","year":2023,"lang":"en","type":"article","venue":"BioMedInformatics","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":41,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"Natural Sciences and Engineering Research Council of Canada; Atlantic Canada Opportunities Agency; New Brunswick Innovation Foundation","keywords":"Diabetic retinopathy; Computer science; Artificial intelligence; Blindness; Fundus (uterus); Machine learning; Deep learning; Retinopathy; Diabetes mellitus; Computer vision; Optometry; Medicine; Ophthalmology","score_opus":0.02480261398870787,"score_gpt":0.3166001306251095,"score_spread":0.29179751663640163,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388126035","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20144165,0.00030696517,0.79167295,0.0002658858,0.00006245291,0.000091446556,0.00012242622,0.0043688645,0.0016673243],"genre_scores_gemma":[0.95136505,0.00006661998,0.047499873,0.000064827596,0.000009465209,0.00003134206,0.000109559085,0.000017668066,0.00083575165],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995493,0.000100558515,0.000033219803,0.0001228537,0.000108940476,0.00008508261],"domain_scores_gemma":[0.99921656,0.00024068865,0.00008373077,0.00013409492,0.00027567416,0.000049325357],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011134917,0.00051786966,0.00063662155,0.00090207654,0.00038545812,0.0008586572,0.0009790895,0.00071353384,0.0011332807],"category_scores_gemma":[0.0021703804,0.00019352107,0.0006528178,0.0005206542,0.00047064177,0.0010994618,0.00091675605,0.00076807337,0.000290082],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007320703,0.00064769725,0.0122711845,0.00006993439,0.0001599732,0.00027251433,0.00011910986,0.4475172,0.01333737,0.0036687597,0.0018718778,0.5193323],"study_design_scores_gemma":[0.000010814488,0.00009797399,0.00063967117,0.0000044118474,0.000015781874,0.00007069074,0.000018835688,0.98986065,0.00661703,0.0024398917,0.0002174411,0.000006816983],"about_ca_topic_score_codex":0.0041618743,"about_ca_topic_score_gemma":0.0023997233,"teacher_disagreement_score":0.0041618743,"about_ca_system_score_codex":0.0009400858,"about_ca_system_score_gemma":0.0009206644,"threshold_uncertainty_score":0.00827527},"labels":[],"label_agreement":null},{"id":"W4389237526","doi":"10.3390/biomedinformatics3040066","title":"Deciphering the Mosaic of Therapeutic Potential: A Scoping Review of Neural Network Applications in Psychotherapy Enhancements","year":2023,"lang":"en","type":"review","venue":"BioMedInformatics","topic":"Mental Health Research Topics","field":"Psychology","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"CINAHL; Psychotherapist; Context (archaeology); Systematic review; MEDLINE; Categorization; PsycINFO; Psychology; Medicine; Artificial intelligence; Computer science; Psychiatry; Psychological intervention","score_opus":0.21301121808990386,"score_gpt":0.529839331408515,"score_spread":0.31682811331861116,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389237526","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005493739,0.996967,0.00050456624,0.0008039888,0.00017881682,0.00027555684,0.000102529775,0.000007329775,0.0006107048],"genre_scores_gemma":[0.00799726,0.98893154,0.0016681526,0.00048959034,0.00008893963,0.00058341806,0.00010545105,0.000005677219,0.00012994089],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.9872543,0.0057020094,0.004081006,0.00059622375,0.0021361115,0.00023040801],"domain_scores_gemma":[0.92464817,0.06276276,0.0055703325,0.0009539595,0.0057504983,0.00031422908],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.022055157,0.0013443788,0.0050755395,0.02149242,0.0011255039,0.0042920397,0.0021398836,0.002764159,0.0041966885],"category_scores_gemma":[0.08553913,0.0010200683,0.006199441,0.015819661,0.0015452661,0.0040448145,0.0025389402,0.0018808498,0.0003585125],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014047009,0.00003393989,0.0004938312,0.7442368,0.0033484823,0.00010682742,0.00056924415,0.00040157346,0.0002252378,0.0021947864,0.0025208604,0.24572794],"study_design_scores_gemma":[0.000035734774,0.00006982571,0.001116499,0.95903134,0.010232071,0.00016931852,0.00038195073,0.00025562118,0.00019330939,0.0015704077,0.0269213,0.00002267054],"about_ca_topic_score_codex":0.007602613,"about_ca_topic_score_gemma":0.02156489,"teacher_disagreement_score":0.022055157,"about_ca_system_score_codex":0.0056558307,"about_ca_system_score_gemma":0.017804764,"threshold_uncertainty_score":0.11664027},"labels":[],"label_agreement":null},{"id":"W4389385283","doi":"10.3390/biomedinformatics3040067","title":"Avatar Intervention for Cannabis Use Disorder in a Patient with Schizoaffective Disorder: A Case Report","year":2023,"lang":"en","type":"article","venue":"BioMedInformatics","topic":"Digital Mental Health Interventions","field":"Psychology","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut national de psychiatrie légale Philippe-Pinel; McGill University; Université de Montréal","funders":"Fonds de Recherche du Québec - Santé; Eli Lilly and Company","keywords":"Schizoaffective disorder; Polysubstance dependence; Intervention (counseling); Avatar; Psychological intervention; Psychology; Cannabis; Abstinence; Clinical psychology; Psychiatry; Cannabis Dependence; Randomized controlled trial; Psychotherapist; Medicine; Substance abuse; Psychosis","score_opus":0.027790096276101184,"score_gpt":0.3575564624692514,"score_spread":0.3297663661931502,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389385283","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.969186,0.0059467037,0.0038795378,0.0066953944,0.0004988853,0.00026877987,0.0001589761,0.00011864842,0.01324703],"genre_scores_gemma":[0.9910986,0.0020626646,0.0019657456,0.0012567393,0.00029342767,0.000037273316,0.00005013607,0.000021066393,0.0032143747],"study_design_codex":"case_report","study_design_gemma":"case_report","domain_scores_codex":[0.9997291,0.000051997686,0.000028231369,0.00004653838,0.0000595529,0.00008451124],"domain_scores_gemma":[0.99960035,0.00012403974,0.00007637211,0.000021810683,0.000018655364,0.00015872219],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017031096,0.0008743544,0.00065112987,0.0010991362,0.0026685758,0.0008437567,0.00054963114,0.0032204022,0.0019504265],"category_scores_gemma":[0.0014191009,0.00039117847,0.00087048934,0.0005363896,0.0010102412,0.0007562195,0.0008771335,0.0020565044,0.00038169505],"study_design_candidate":"case_report","study_design_consensus":"case_report","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006145579,0.00018720173,0.0044306275,0.000055929617,0.00001603953,0.98794425,0.000815716,0.00009692938,0.0010228176,0.0004538838,0.00043103046,0.004484199],"study_design_scores_gemma":[0.00001901229,0.00017034325,0.003177853,0.000032528384,0.000016380915,0.99363774,0.0005137971,0.00039432195,0.00050052843,0.00026311414,0.0012583289,0.000015998476],"about_ca_topic_score_codex":0.0048844786,"about_ca_topic_score_gemma":0.010709887,"teacher_disagreement_score":0.0048844786,"about_ca_system_score_codex":0.0011437725,"about_ca_system_score_gemma":0.00089203956,"threshold_uncertainty_score":0.0097121},"labels":[],"label_agreement":null},{"id":"W4389488961","doi":"10.3390/biomedinformatics3040070","title":"Transforming Drug Design: Innovations in Computer-Aided Discovery for Biosimilar Agents","year":2023,"lang":"en","type":"article","venue":"BioMedInformatics","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University Canada West","funders":"","keywords":"Biosimilar; Drug discovery; Computer science; Data science; Risk analysis (engineering); Transformative learning; Drug development; Management science; Engineering; Drug; Medicine; Biotechnology; Bioinformatics; Pharmacology; Biology","score_opus":0.07765172571089067,"score_gpt":0.33580290381415295,"score_spread":0.25815117810326227,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389488961","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009522795,0.036402136,0.92924654,0.0075806505,0.00046094463,0.00024509712,0.00022690879,0.0010203269,0.015294653],"genre_scores_gemma":[0.12206154,0.04881183,0.8227767,0.0013513069,0.0003024908,0.00047714481,0.0004523765,0.00029631142,0.0034703456],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99836034,0.00091436965,0.00007503688,0.00014792099,0.00045339513,0.000048998532],"domain_scores_gemma":[0.99744797,0.0018894922,0.00012363013,0.00023773267,0.00024695194,0.000054219952],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033189387,0.0009786922,0.001173022,0.0015965169,0.00040447747,0.0020681925,0.0012409266,0.0012683433,0.003285649],"category_scores_gemma":[0.0073269806,0.0005875657,0.001189361,0.0020132056,0.0019289174,0.0024720633,0.0017241169,0.0033378953,0.0011905807],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015411295,0.00017889634,0.0009642463,0.0019614268,0.00015561879,0.00018410363,0.00022705985,0.22035456,0.0068915947,0.4859741,0.008437605,0.27451664],"study_design_scores_gemma":[0.00019073751,0.0001852742,0.00027405604,0.0004798425,0.000065421744,0.00032486755,0.000096791504,0.5102676,0.0068780137,0.34776366,0.1333969,0.00007677114],"about_ca_topic_score_codex":0.0010561851,"about_ca_topic_score_gemma":0.0010497561,"teacher_disagreement_score":0.0033189387,"about_ca_system_score_codex":0.0011382066,"about_ca_system_score_gemma":0.002067976,"threshold_uncertainty_score":0.017552435},"labels":[],"label_agreement":null},{"id":"W4389538079","doi":"10.3390/biomedinformatics3040071","title":"Optimized FIR Filter Using Genetic Algorithms: A Case Study of ECG Signals Filter Optimization","year":2023,"lang":"en","type":"article","venue":"BioMedInformatics","topic":"Advanced Adaptive Filtering Techniques","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Finite impulse response; Filter design; Genetic algorithm; Computer science; Root-raised-cosine filter; Filter (signal processing); Digital filter; Adaptive filter; Algorithm; Half-band filter; Electronic engineering; Kernel adaptive filter; Signal processing; Bandwidth (computing); Digital signal processing; Engineering; Computer hardware; Telecommunications; Machine learning; Computer vision","score_opus":0.04914661836207609,"score_gpt":0.29080986548249127,"score_spread":0.24166324712041518,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389538079","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5473095,0.0009642407,0.43797243,0.0005506168,0.000034789067,0.00015832165,0.00010803716,0.0003330814,0.012569001],"genre_scores_gemma":[0.82701087,0.00029326772,0.16911589,0.000051808016,0.000011128597,0.000077033845,0.000061392486,0.00003056367,0.0033479724],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99962723,0.00016352774,0.00001676894,0.000042186028,0.00010618338,0.000044062115],"domain_scores_gemma":[0.9989894,0.0007644523,0.00004660606,0.000042998905,0.00013459829,0.000022005448],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010773162,0.0004942213,0.00051155937,0.00058353203,0.0003710804,0.0005758201,0.0004587889,0.001455334,0.0007197055],"category_scores_gemma":[0.0018692252,0.00017094758,0.0005549748,0.00058719225,0.0003690513,0.00028465007,0.0002413492,0.00040054292,0.00008518502],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000764225,0.00012593006,0.0017273222,0.0000806733,0.000052854546,0.0005221133,0.00008950934,0.95347476,0.0037200323,0.004217046,0.00041258842,0.03550073],"study_design_scores_gemma":[0.000023506029,0.00012496495,0.00057033676,0.0000070694236,0.00002285283,0.000094728945,0.000046414814,0.9931913,0.0038251933,0.0011507098,0.000934429,0.000008428166],"about_ca_topic_score_codex":0.0056481957,"about_ca_topic_score_gemma":0.005484445,"teacher_disagreement_score":0.0056481957,"about_ca_system_score_codex":0.00052207574,"about_ca_system_score_gemma":0.00055847835,"threshold_uncertainty_score":0.011230648},"labels":[],"label_agreement":null},{"id":"W4390509856","doi":"10.3390/biomedinformatics4010006","title":"Biomedical Informatics: State of the Art, Challenges, and Opportunities","year":2024,"lang":"en","type":"article","venue":"BioMedInformatics","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; University of Manitoba","keywords":"Health informatics; Informatics; Engineering informatics; Multidisciplinary approach; Translational research informatics; Data science; Field (mathematics); Health Administration Informatics; Business informatics; Biomedicine; Translational bioinformatics; Computer science; Big data; Situated; Intersection (aeronautics); Health care; Artificial intelligence; Management science; Bioinformatics; Mathematics; Engineering; Data mining; Social science; Political science; Sociology; Biology","score_opus":0.050976904078499585,"score_gpt":0.2801951133638326,"score_spread":0.22921820928533304,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390509856","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0031542575,0.8107563,0.024142867,0.14702459,0.002875876,0.00003729878,0.00013999795,0.0002070443,0.011661696],"genre_scores_gemma":[0.05170781,0.8893963,0.028937759,0.014642317,0.012314627,0.00009989731,0.00031638946,0.00009917834,0.0024857838],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9930607,0.0031904716,0.0004963835,0.0008089043,0.0020436053,0.0003999043],"domain_scores_gemma":[0.9501532,0.04011416,0.0012246118,0.0016784541,0.0049281325,0.0019013619],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01988992,0.0008302381,0.0012828979,0.0040064286,0.0021078635,0.012239056,0.0024892571,0.0060576657,0.0060783084],"category_scores_gemma":[0.01952723,0.00068215595,0.0007447047,0.004913888,0.008829693,0.024488458,0.004966148,0.009407291,0.0021949983],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012598801,0.00016189048,0.0022407386,0.0058891224,0.0000528726,0.00016817034,0.0013373606,0.0017856873,0.00092591665,0.24880552,0.050748836,0.68775785],"study_design_scores_gemma":[0.000024878016,0.00015201456,0.0019287992,0.01195156,0.000059963768,0.0009899983,0.0051290956,0.008740313,0.00080463686,0.38427714,0.5857841,0.00015758893],"about_ca_topic_score_codex":0.0017539038,"about_ca_topic_score_gemma":0.0021997823,"teacher_disagreement_score":0.01988992,"about_ca_system_score_codex":0.0024510988,"about_ca_system_score_gemma":0.0062270677,"threshold_uncertainty_score":0.105189204},"labels":[],"label_agreement":null},{"id":"W4390788709","doi":"10.3390/biomedinformatics4010014","title":"Factors Associated with Unplanned Hospital Readmission after Discharge: A Descriptive and Predictive Study Using Electronic Health Record Data","year":2024,"lang":"en","type":"article","venue":"BioMedInformatics","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université de Moncton","funders":"Natural Sciences and Engineering Research Council of Canada; Fondation de la recherche en santé du Nouveau-Brunswick","keywords":"Medicine; Emergency medicine; Hospital readmission; Hospital discharge; Comorbidity; Health care; Descriptive statistics; Electronic health record; Medical emergency; Intensive care medicine; Internal medicine","score_opus":0.05943633180117005,"score_gpt":0.3244137911842424,"score_spread":0.2649774593830724,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390788709","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99913615,0.00006713987,0.0001762824,0.000028147864,0.0000031599163,0.000025628093,0.00043688706,0.0000037407742,0.00012280022],"genre_scores_gemma":[0.99879646,0.00007873508,0.00021933351,0.000021086109,0.000008051653,0.00003603256,0.0007844233,0.0000025777442,0.000053218966],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99851173,0.00047056092,0.0002984441,0.00022316285,0.00032827788,0.00016785704],"domain_scores_gemma":[0.99261355,0.003587076,0.0022103149,0.0005461214,0.00065401307,0.0003889555],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024928604,0.0003185937,0.00044349927,0.0019486303,0.00041451087,0.00088289153,0.0006114061,0.00050527445,0.0009122203],"category_scores_gemma":[0.008313785,0.00038365973,0.0011169055,0.002110465,0.00042315904,0.0011808989,0.000823598,0.0011007435,0.00023223578],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012367706,0.00015900453,0.9986185,0.000010797488,0.00005596689,0.000038609884,0.0000693821,0.00007191674,0.000032188163,0.000015362946,0.00005705311,0.0007475672],"study_design_scores_gemma":[0.000014716274,0.00028737722,0.9964832,0.000015495572,0.000056945504,0.00020766965,0.0005293652,0.0021627727,0.00007977409,0.000039852952,0.00011282665,0.000009980098],"about_ca_topic_score_codex":0.005967548,"about_ca_topic_score_gemma":0.0065865014,"teacher_disagreement_score":0.005967548,"about_ca_system_score_codex":0.00056098663,"about_ca_system_score_gemma":0.0007222814,"threshold_uncertainty_score":0.013183653},"labels":[],"label_agreement":null},{"id":"W4391025999","doi":"10.3390/biomedinformatics4010015","title":"Deep Machine Learning for Medical Diagnosis, Application to Lung Cancer Detection: A Review","year":2024,"lang":"en","type":"review","venue":"BioMedInformatics","topic":"Lung Cancer Diagnosis and Treatment","field":"Medicine","cited_by":98,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"New Brunswick Innovation Foundation; Fondation de la recherche en santé du Nouveau-Brunswick","keywords":"Deep learning; Artificial intelligence; Machine learning; Computer science; Interpretability; Convolutional neural network","score_opus":0.025486598695986695,"score_gpt":0.39959557028761356,"score_spread":0.37410897159162687,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391025999","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00049938244,0.9898998,0.0062480075,0.001290974,0.00027632964,0.00001701837,0.00007366559,0.000055853372,0.0016389822],"genre_scores_gemma":[0.0038620736,0.9913611,0.003121859,0.0005246437,0.00033937907,0.00002124089,0.000119106335,0.000013199212,0.00063741085],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99963224,0.00007747768,0.000055465087,0.00006971837,0.00013904336,0.000026077332],"domain_scores_gemma":[0.99885607,0.0007910728,0.00007096451,0.00002919828,0.00021706008,0.000035637997],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012010466,0.000857671,0.0009030754,0.0018015724,0.00021751164,0.0009743348,0.00094246725,0.0012503302,0.0031717587],"category_scores_gemma":[0.0025613874,0.00041497638,0.0008185698,0.0021382184,0.000552107,0.0014142172,0.00083636405,0.0017299685,0.0015288055],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000423388,0.000054848613,0.0005288892,0.009356921,0.00012988319,0.00010227026,0.000055977307,0.0029281052,0.00097579765,0.0059239925,0.023800889,0.9561],"study_design_scores_gemma":[0.0000316135,0.000255134,0.0024994155,0.011644681,0.00037685293,0.0013756006,0.00009625583,0.010242297,0.0026883616,0.016441174,0.9542467,0.00010185101],"about_ca_topic_score_codex":0.0023837187,"about_ca_topic_score_gemma":0.0024155097,"teacher_disagreement_score":0.0031717587,"about_ca_system_score_codex":0.00073685043,"about_ca_system_score_gemma":0.001575374,"threshold_uncertainty_score":0.01061064},"labels":[],"label_agreement":null},{"id":"W4391598858","doi":"10.3390/biomedinformatics4010024","title":"Ensemble Methods to Optimize Automated Text Classification in Avatar Therapy","year":2024,"lang":"en","type":"article","venue":"BioMedInformatics","topic":"Digital Mental Health Interventions","field":"Psychology","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut national de psychiatrie légale Philippe-Pinel; Université de Montréal; Institut Universitaire en Santé Mentale de Québec","funders":"Otsuka Canada Pharmaceutical; Canada First Research Excellence Fund","keywords":"Avatar; Computer science; Artificial intelligence; Natural language processing; Machine learning; Pattern recognition (psychology); Human–computer interaction","score_opus":0.11003546193994432,"score_gpt":0.4929203586231805,"score_spread":0.3828848966832362,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391598858","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11824566,0.0016456909,0.8747434,0.0006431859,0.00024222142,0.00021621956,0.00048651078,0.0023673712,0.00140968],"genre_scores_gemma":[0.7052408,0.0005027068,0.28850576,0.0002837125,0.00026985258,0.0003903449,0.0016693076,0.00019700048,0.0029405043],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983364,0.0006826711,0.00015433716,0.0004177612,0.00025876035,0.00015013595],"domain_scores_gemma":[0.99343675,0.0043028155,0.00038247122,0.00046510287,0.0012473797,0.00016540098],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004995186,0.0013428605,0.0018934213,0.0018394548,0.00066981855,0.001362542,0.0016690309,0.0015245774,0.0015531473],"category_scores_gemma":[0.010872437,0.00048057496,0.0011623163,0.0014271697,0.00037305267,0.0016280931,0.0010682112,0.0019139561,0.00081298995],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003989924,0.00024162489,0.0060583483,0.00012496058,0.0002955763,0.000083976855,0.000235452,0.49100974,0.0025237617,0.0016337647,0.0034668252,0.493927],"study_design_scores_gemma":[0.000005570422,0.000046028927,0.00040459645,0.0000086435075,0.000016025084,0.000011711932,0.000020893953,0.9973037,0.00050760864,0.0013904665,0.00027938184,0.000005363632],"about_ca_topic_score_codex":0.006544593,"about_ca_topic_score_gemma":0.005567092,"teacher_disagreement_score":0.006544593,"about_ca_system_score_codex":0.0010248262,"about_ca_system_score_gemma":0.0010935267,"threshold_uncertainty_score":0.026417375},"labels":[],"label_agreement":null},{"id":"W4392807024","doi":"10.3390/biomedinformatics4010047","title":"Generative Pre-Trained Transformer-Empowered Healthcare Conversations: Current Trends, Challenges, and Future Directions in Large Language Model-Enabled Medical Chatbots","year":2024,"lang":"en","type":"article","venue":"BioMedInformatics","topic":"AI in Service Interactions","field":"Computer Science","cited_by":70,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Princess Margaret Cancer Centre; University of Toronto; Toronto Metropolitan University; University Health Network","funders":"Canadian Institutes of Health Research; York University","keywords":"Generative grammar; Transformer; Computer science; Health care; Data science; Artificial intelligence; Engineering; Political science; Electrical engineering","score_opus":0.021952796929736177,"score_gpt":0.3209466085783576,"score_spread":0.29899381164862143,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392807024","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.035882447,0.04839373,0.86137366,0.013323388,0.0011478323,0.00062389893,0.0007729624,0.006811284,0.031670813],"genre_scores_gemma":[0.54362005,0.024707561,0.4066798,0.0043896534,0.0009623991,0.0016163561,0.0023608739,0.0011202781,0.014542941],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99118924,0.006405294,0.00031182382,0.0008299766,0.0010126648,0.00025110046],"domain_scores_gemma":[0.9704592,0.026060129,0.0005163512,0.0013498267,0.0011051686,0.00050928106],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011175511,0.00095688226,0.001066058,0.0012746286,0.000850761,0.0041102143,0.0035378544,0.0025129917,0.009301511],"category_scores_gemma":[0.03012649,0.0005946774,0.0009445443,0.00079941185,0.0025068508,0.007133941,0.0051491363,0.0023569255,0.0038708597],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00059885724,0.0004322108,0.0036790855,0.009069762,0.00020395523,0.0006864171,0.015206863,0.02298453,0.014979472,0.0993672,0.017128125,0.8156636],"study_design_scores_gemma":[0.00019190265,0.0008517164,0.0030764937,0.005304735,0.00028335527,0.002083648,0.009780975,0.27499753,0.02057785,0.23084104,0.45157087,0.0004399714],"about_ca_topic_score_codex":0.0015464857,"about_ca_topic_score_gemma":0.0014964617,"teacher_disagreement_score":0.011175511,"about_ca_system_score_codex":0.0015528877,"about_ca_system_score_gemma":0.0025194604,"threshold_uncertainty_score":0.059102476},"labels":[],"label_agreement":null},{"id":"W4393387378","doi":"10.3390/biomedinformatics4020053","title":"A Methodological Approach to Extracting Patterns of Service Utilization from a Cross-Continuum High Dimensional Healthcare Dataset to Support Care Delivery Optimization for Patients with Complex Problems","year":2024,"lang":"en","type":"article","venue":"BioMedInformatics","topic":"Digital Mental Health Interventions","field":"Psychology","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Dalhousie University; University of Victoria","funders":"","keywords":"Healthcare delivery; Health care; Computer science; Continuum of care; Service delivery framework; Health care delivery; Data science; Service (business); Business; Political science","score_opus":0.26947824496667316,"score_gpt":0.4519047539399708,"score_spread":0.18242650897329765,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393387378","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11438493,0.00035612195,0.87568593,0.0017289101,0.000055578803,0.0010544936,0.004926461,0.0007196153,0.0010879358],"genre_scores_gemma":[0.3106752,0.000112166636,0.6797577,0.00021533047,0.000038550483,0.001259881,0.0076409196,0.000040540544,0.00025972512],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99329054,0.0037523995,0.00063328206,0.0014499552,0.0007018844,0.00017189447],"domain_scores_gemma":[0.982053,0.009348459,0.0032248138,0.0028675124,0.0020340313,0.00047216954],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008304618,0.00080118526,0.00051984494,0.008055184,0.0011829119,0.0021277678,0.0017031651,0.0011925475,0.0007169125],"category_scores_gemma":[0.034249812,0.00038952273,0.0014916258,0.0071399794,0.001094948,0.0015218505,0.0026864621,0.0013723517,0.00029201136],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045367202,0.0014998625,0.44484323,0.0017980918,0.0017827017,0.00071240094,0.004080279,0.09998943,0.011797762,0.042666685,0.01549738,0.37487856],"study_design_scores_gemma":[0.00011025928,0.00024988945,0.076272726,0.0002468119,0.0002520853,0.00039934504,0.002759628,0.81862307,0.0036819875,0.08496644,0.012346152,0.000091641465],"about_ca_topic_score_codex":0.0063821073,"about_ca_topic_score_gemma":0.0153325945,"teacher_disagreement_score":0.008304618,"about_ca_system_score_codex":0.001347624,"about_ca_system_score_gemma":0.0035112016,"threshold_uncertainty_score":0.043919504},"labels":[],"label_agreement":null},{"id":"W4394691258","doi":"10.3390/biomedinformatics4020060","title":"Analyzing Patterns of Service Utilization Using Graph Topology to Understand the Dynamic of the Engagement of Patients with Complex Problems with Health Services","year":2024,"lang":"en","type":"article","venue":"BioMedInformatics","topic":"Mental Health Research Topics","field":"Psychology","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University; University of Victoria","funders":"","keywords":"Graph; Topology (electrical circuits); Computer science; Health services; Distributed computing; Theoretical computer science; Mathematics; Medicine; Combinatorics; Environmental health","score_opus":0.12800679703333512,"score_gpt":0.40883967513338754,"score_spread":0.2808328781000524,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394691258","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89734066,0.00065592723,0.08884277,0.0016772763,0.000048015878,0.000117513046,0.0054037804,0.0006258991,0.0052880924],"genre_scores_gemma":[0.97364694,0.0002652397,0.023660714,0.00003932013,0.000013168672,0.000049560083,0.0018979226,0.00003591163,0.0003914139],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9995363,0.00021893549,0.00003594471,0.00009633368,0.00006928736,0.000043226348],"domain_scores_gemma":[0.9939699,0.004224732,0.0008475923,0.00038012487,0.00035105553,0.00022660487],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008941385,0.00036681452,0.00022255127,0.0049038744,0.00035579747,0.0013849825,0.00042104392,0.00054174947,0.0030387898],"category_scores_gemma":[0.008989344,0.0001591521,0.00043345834,0.004151013,0.00045850585,0.0020243246,0.00073138834,0.0005490758,0.00033715734],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00052639196,0.00025313735,0.65323514,0.0006022275,0.00043503795,0.000814018,0.008584017,0.13030653,0.0055949255,0.033555713,0.008125572,0.15796727],"study_design_scores_gemma":[0.000031359283,0.00024476787,0.27762815,0.00020861246,0.00017193968,0.0011308478,0.014258577,0.608819,0.0026057072,0.07642611,0.01837216,0.00010266733],"about_ca_topic_score_codex":0.0073329555,"about_ca_topic_score_gemma":0.010566998,"teacher_disagreement_score":0.0073329555,"about_ca_system_score_codex":0.0007557001,"about_ca_system_score_gemma":0.0005296089,"threshold_uncertainty_score":0.014580548},"labels":[],"label_agreement":null},{"id":"W4394877296","doi":"10.3390/biomedinformatics4020062","title":"Recent Advances in Large Language Models for Healthcare","year":2024,"lang":"en","type":"article","venue":"BioMedInformatics","topic":"Topic Modeling","field":"Computer Science","cited_by":93,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"New Brunswick Innovation Foundation; Fondation de la recherche en santé du Nouveau-Brunswick","keywords":"Field (mathematics); Health care; Variety (cybernetics); Computer science; Domain (mathematical analysis); Data science; Medical care; Management science; Risk analysis (engineering); Medicine; Political science; Artificial intelligence; Engineering","score_opus":0.03239555851291891,"score_gpt":0.3223995153578027,"score_spread":0.2900039568448838,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394877296","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005617961,0.095470145,0.84847546,0.03251505,0.0015536308,0.00012019045,0.002006729,0.0035830813,0.010657728],"genre_scores_gemma":[0.23406215,0.16129039,0.5610522,0.009658058,0.009809866,0.0006085376,0.008470075,0.0016769076,0.013371744],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9965701,0.0019124355,0.0002464075,0.00053912244,0.0006284353,0.00010347578],"domain_scores_gemma":[0.9826533,0.014012199,0.00045694478,0.001278654,0.0013137532,0.00028509248],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0065315063,0.0012282119,0.00142322,0.0025473384,0.00043648103,0.003739378,0.0022932487,0.0018149553,0.006289649],"category_scores_gemma":[0.023785967,0.0008332474,0.0018412839,0.0033680336,0.0012436267,0.0051518898,0.0022234388,0.0040043667,0.0038154891],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027635336,0.0001535384,0.003186113,0.0018685204,0.0004398314,0.0002617349,0.00046611464,0.13269912,0.0016832313,0.15962514,0.054116923,0.6452234],"study_design_scores_gemma":[0.00003472249,0.00007121005,0.0009787729,0.00050715246,0.00012872653,0.00025540168,0.0001070912,0.59254974,0.0010727084,0.2542858,0.1499074,0.00010124156],"about_ca_topic_score_codex":0.0073100645,"about_ca_topic_score_gemma":0.0059049972,"teacher_disagreement_score":0.0073100645,"about_ca_system_score_codex":0.0025859,"about_ca_system_score_gemma":0.002598318,"threshold_uncertainty_score":0.034542322},"labels":[],"label_agreement":null},{"id":"W4396720033","doi":"10.3390/biomedinformatics4020065","title":"Diagnostic Tool for Early Detection of Rheumatic Disorders Using Machine Learning Algorithm and Predictive Models","year":2024,"lang":"en","type":"article","venue":"BioMedInformatics","topic":"Digital Imaging for Blood Diseases","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University; University of Ottawa","funders":"","keywords":"Medicine; Machine learning; Rheumatoid arthritis; Disease; Osteoarthritis; Artificial intelligence; Algorithm; Computer science; Pathology; Internal medicine","score_opus":0.010775507267268093,"score_gpt":0.2299335592247297,"score_spread":0.2191580519574616,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396720033","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15951267,0.0043549533,0.81352836,0.002963227,0.00036954827,0.00040199523,0.004069711,0.009154903,0.005644617],"genre_scores_gemma":[0.75699383,0.0011507114,0.234602,0.00045301564,0.00020510177,0.00023129702,0.0032221053,0.00009160445,0.003050354],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993191,0.00016682855,0.00007749833,0.00016205534,0.00020982901,0.00006466403],"domain_scores_gemma":[0.99763954,0.001429385,0.00025207282,0.00011728278,0.00047967705,0.00008203321],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012256346,0.00096467126,0.00077096187,0.0039140857,0.000357898,0.0011064095,0.00083367265,0.0011319482,0.0026571152],"category_scores_gemma":[0.0048178667,0.00025492354,0.00078311784,0.0011731761,0.00019323197,0.0006772178,0.00053462276,0.0010790629,0.0012113127],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00075353705,0.001067357,0.0922104,0.0005437336,0.00035855887,0.0009903613,0.00012336868,0.20342746,0.010504541,0.0029925103,0.020696739,0.66633147],"study_design_scores_gemma":[0.000024295372,0.00008980425,0.0057789623,0.000067279776,0.0000581704,0.00031908252,0.000030568637,0.9854419,0.0038464556,0.0025412696,0.0017770046,0.000025136922],"about_ca_topic_score_codex":0.005896659,"about_ca_topic_score_gemma":0.0051000044,"teacher_disagreement_score":0.005896659,"about_ca_system_score_codex":0.0007765882,"about_ca_system_score_gemma":0.001119326,"threshold_uncertainty_score":0.011724651},"labels":[],"label_agreement":null},{"id":"W4396796398","doi":"10.3390/biomedinformatics4020069","title":"A Smartphone-Based Algorithm for L Test Subtask Segmentation","year":2024,"lang":"en","type":"article","venue":"BioMedInformatics","topic":"Software Testing and Debugging Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Test (biology); Artificial intelligence; Segmentation; Algorithm; Computer vision; Geology","score_opus":0.019163561674323558,"score_gpt":0.28097800109259485,"score_spread":0.2618144394182713,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396796398","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04258592,0.00040302466,0.93728447,0.0002610935,0.000104210405,0.00052335433,0.0009332585,0.015660673,0.002243997],"genre_scores_gemma":[0.23519625,0.00017148274,0.75841737,0.0002299297,0.000049772236,0.00074424123,0.001701034,0.000462137,0.003027741],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992805,0.000107530694,0.000090073896,0.000253786,0.00021269078,0.00005551705],"domain_scores_gemma":[0.99804544,0.00070247403,0.0002063205,0.00014958734,0.0007970512,0.000099146906],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006893254,0.0012165577,0.000740777,0.0019717913,0.00043414478,0.0009857927,0.0009965668,0.00092704437,0.00528592],"category_scores_gemma":[0.004621288,0.00030357135,0.00050744327,0.00080849294,0.00025067767,0.00049868843,0.0008204831,0.0006041028,0.0032925976],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006935214,0.00017709183,0.0095185265,0.00017438369,0.000082711354,0.00028130107,0.00019788134,0.01205036,0.03346172,0.00087481947,0.0134766195,0.9290111],"study_design_scores_gemma":[0.00024458204,0.00046976365,0.017201358,0.0000799553,0.000091856484,0.0012119546,0.00019054099,0.91237676,0.048601884,0.0037382492,0.015699565,0.00009358282],"about_ca_topic_score_codex":0.0056489534,"about_ca_topic_score_gemma":0.007509106,"teacher_disagreement_score":0.0056489534,"about_ca_system_score_codex":0.0005940874,"about_ca_system_score_gemma":0.0011902461,"threshold_uncertainty_score":0.017683148},"labels":[],"label_agreement":null},{"id":"W4400679410","doi":"10.3390/biomedinformatics4030092","title":"Chauhan Weighted Trajectory Analysis Reduces Sample Size Requirements and Expedites Time-to-Efficacy Signals in Advanced Cancer Clinical Trials","year":2024,"lang":"en","type":"article","venue":"BioMedInformatics","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Trajectory; Sample size determination; Clinical trial; Sample (material); Medicine; Cancer; Computer science; Mathematics; Statistics; Internal medicine; Physics","score_opus":0.12285305476525497,"score_gpt":0.46877097677539725,"score_spread":0.34591792201014226,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400679410","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2968766,0.0017037384,0.69104314,0.0017815946,0.00028817318,0.0035828436,0.0005846269,0.0010275337,0.0031116917],"genre_scores_gemma":[0.8003076,0.00022188717,0.19381957,0.0006490764,0.00005162376,0.0038526235,0.00042622126,0.000092297545,0.00057912356],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.92946,0.064783804,0.0015932195,0.0016190066,0.0020414798,0.0005025206],"domain_scores_gemma":[0.7533427,0.22671801,0.007280436,0.007905817,0.003567737,0.0011853647],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.08947476,0.0011925283,0.0018663513,0.0009892385,0.00069129246,0.0011825897,0.0015747306,0.0015392115,0.002551956],"category_scores_gemma":[0.18170796,0.000735983,0.0028680176,0.00082979753,0.0017438212,0.0020627975,0.0019753838,0.0028289885,0.0002585235],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.021166354,0.00094274624,0.032215312,0.0015400966,0.004825788,0.00038259133,0.00083842396,0.78334767,0.008398885,0.031316947,0.0034109377,0.1116142],"study_design_scores_gemma":[0.0031319682,0.0050263596,0.0056943544,0.00014765779,0.0009831068,0.00010301972,0.00007089584,0.9458259,0.004992681,0.029383387,0.004567366,0.00007327898],"about_ca_topic_score_codex":0.002302257,"about_ca_topic_score_gemma":0.002068472,"teacher_disagreement_score":0.91052526,"about_ca_system_score_codex":0.0015312086,"about_ca_system_score_gemma":0.004251792,"threshold_uncertainty_score":0.4731934},"labels":[],"label_agreement":null},{"id":"W4401482476","doi":"10.3390/biomedinformatics4030103","title":"Approaches to Extracting Patterns of Service Utilization for Patients with Complex Conditions: Graph Community Detection vs. Natural Language Processing Clustering","year":2024,"lang":"en","type":"article","venue":"BioMedInformatics","topic":"Chronic Disease Management Strategies","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University; Simon Fraser University; University of Victoria","funders":"","keywords":"Computer science; Cluster analysis; Graph; Service (business); Service system; Service quality; Artificial intelligence; Data science; Machine learning; Natural language processing; Data mining; Theoretical computer science","score_opus":0.1516485618966432,"score_gpt":0.33923959603517195,"score_spread":0.18759103413852876,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401482476","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.123887144,0.001057903,0.86448973,0.0028344237,0.000080064456,0.0011784037,0.0022633595,0.0013859952,0.002822975],"genre_scores_gemma":[0.33609843,0.00029774706,0.6603689,0.00025150986,0.00004869997,0.000504075,0.0018445738,0.0000928862,0.0004933321],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9962721,0.0018510979,0.0003223691,0.0008478283,0.0005644109,0.00014227546],"domain_scores_gemma":[0.9799448,0.014691756,0.0022240058,0.0010918949,0.0016755511,0.0003720073],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0056036343,0.0008023336,0.00060877437,0.013253456,0.0011722087,0.0027600783,0.001342463,0.00096220063,0.0014447379],"category_scores_gemma":[0.023368128,0.00034555135,0.0014331724,0.0076968665,0.0010975652,0.0019298547,0.00165909,0.0010366628,0.00039719796],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006801916,0.00084247725,0.12093739,0.0023515336,0.00084626797,0.0007052206,0.0070954724,0.06683276,0.011091198,0.031431224,0.012598507,0.7445878],"study_design_scores_gemma":[0.00012607592,0.00016912496,0.05037692,0.0003938695,0.00031228564,0.0008208492,0.0067544044,0.79845506,0.005509431,0.12448722,0.012410645,0.00018410396],"about_ca_topic_score_codex":0.015236302,"about_ca_topic_score_gemma":0.026406175,"teacher_disagreement_score":0.015236302,"about_ca_system_score_codex":0.0023605814,"about_ca_system_score_gemma":0.0029382734,"threshold_uncertainty_score":0.030295193},"labels":[],"label_agreement":null},{"id":"W4401994484","doi":"10.3390/biomedinformatics4030106","title":"Diffusion-Based Image Synthesis or Traditional Augmentation for Enriching Musculoskeletal Ultrasound Datasets","year":2024,"lang":"en","type":"article","venue":"BioMedInformatics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Novo Nordisk","keywords":"Diffusion; Ultrasound; Computer science; Image (mathematics); Artificial intelligence; Data science; Computer vision; Medicine; Radiology; Physics","score_opus":0.030661816417430664,"score_gpt":0.3204265865983731,"score_spread":0.28976477018094243,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401994484","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1364269,0.0016625326,0.8513442,0.0013701395,0.0002287679,0.00030556874,0.0017591197,0.0037965686,0.0031061943],"genre_scores_gemma":[0.5474431,0.0011544918,0.44448453,0.0006362595,0.00018251737,0.00034632772,0.0034323628,0.00039037797,0.0019300663],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994217,0.00021503794,0.000042924858,0.00016572498,0.00010798278,0.00004674711],"domain_scores_gemma":[0.9971733,0.0015010749,0.000300298,0.0006246637,0.00029643395,0.000104216706],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023880657,0.0009974944,0.0006103375,0.0010340037,0.00027183435,0.00093847135,0.0010716323,0.0011082819,0.0018563573],"category_scores_gemma":[0.0074739736,0.00041290594,0.0010877154,0.00076152466,0.0009442964,0.0015020188,0.001706252,0.0014079477,0.0006251357],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014175699,0.00035656008,0.009979685,0.001331874,0.0002628525,0.0004794788,0.00044497853,0.41570637,0.13465051,0.012453605,0.008242392,0.4146741],"study_design_scores_gemma":[0.00006426947,0.00036424398,0.0021930155,0.0000970551,0.00007945958,0.0003984617,0.00007929653,0.9208391,0.053189017,0.013845773,0.008792834,0.000057520305],"about_ca_topic_score_codex":0.0011225044,"about_ca_topic_score_gemma":0.0016645835,"teacher_disagreement_score":0.0023880657,"about_ca_system_score_codex":0.0004815985,"about_ca_system_score_gemma":0.0006603191,"threshold_uncertainty_score":0.012629449},"labels":[],"label_agreement":null},{"id":"W4402578380","doi":"10.3390/biomedinformatics4030112","title":"Cross-National Analysis of Opioid Prescribing Patterns: Enhancements and Insights from the OralOpioids R Package in Canada and the United States","year":2024,"lang":"en","type":"article","venue":"BioMedInformatics","topic":"Opioid Use Disorder Treatment","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Cross over; Cross country; Opioid; R package; Political science; Medicine; Economics; Statistics; Mathematics; Demographic economics; Internal medicine","score_opus":0.01433500420857666,"score_gpt":0.27006161789628985,"score_spread":0.2557266136877132,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402578380","genre_codex":"dataset","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30925897,0.0040430157,0.102222204,0.015780717,0.00040312286,0.0014098478,0.44602475,0.05543449,0.06542278],"genre_scores_gemma":[0.46209294,0.00310881,0.25032562,0.003002775,0.00012776532,0.0008360118,0.25714973,0.010859835,0.012496561],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99367684,0.0017175146,0.0004865618,0.0008957455,0.0026209783,0.0006023423],"domain_scores_gemma":[0.97643334,0.008198459,0.0016168553,0.0033286018,0.009436598,0.0009861795],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009467133,0.0012987297,0.00089119474,0.005064852,0.0014040692,0.0031928779,0.0026733724,0.0005389811,0.0073514828],"category_scores_gemma":[0.03834392,0.0008127905,0.0022096382,0.008374943,0.00083709275,0.0012793588,0.0033103202,0.0014183559,0.002266826],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016361157,0.00040398783,0.40377685,0.0013983247,0.0031089622,0.00077756745,0.0023464535,0.045497287,0.0023779091,0.015534526,0.3160465,0.20709541],"study_design_scores_gemma":[0.00053609733,0.0002319123,0.5316306,0.0009393273,0.0010036139,0.00058099185,0.0022466918,0.13086009,0.006430092,0.0071112686,0.3179705,0.00045887468],"about_ca_topic_score_codex":0.94472283,"about_ca_topic_score_gemma":0.9555713,"teacher_disagreement_score":0.05527717,"about_ca_system_score_codex":0.012737391,"about_ca_system_score_gemma":0.050275587,"threshold_uncertainty_score":0.11120534},"labels":[],"label_agreement":null},{"id":"W4403513407","doi":"10.3390/biomedinformatics4040113","title":"Evaluating COVID-19 Vaccine Efficacy Using Kaplan–Meier Survival Analysis","year":2024,"lang":"en","type":"article","venue":"BioMedInformatics","topic":"SARS-CoV-2 and COVID-19 Research","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Alberta Oil Sands Technology and Research Authority; University of Guelph; McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Coronavirus disease 2019 (COVID-19); Survival analysis; Virology; 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Medicine; Internal medicine; Infectious disease (medical specialty); Disease","score_opus":0.1784020813150458,"score_gpt":0.4806786236201714,"score_spread":0.3022765423051256,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403513407","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.77540493,0.0071659177,0.18492162,0.0006329626,0.00017781144,0.0016968125,0.017228762,0.0011385148,0.011632598],"genre_scores_gemma":[0.969865,0.0009859166,0.02216464,0.00007677063,0.000047943064,0.00069121056,0.0043285205,0.00008783332,0.0017520873],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9961392,0.0017292845,0.0004579618,0.00045793253,0.00093923626,0.00027631054],"domain_scores_gemma":[0.9821998,0.011448191,0.0035698726,0.0009910838,0.0015514267,0.00023969401],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.017392607,0.00061573886,0.00087841484,0.0021110151,0.00022824516,0.00089592504,0.0005656821,0.0004406071,0.0052516325],"category_scores_gemma":[0.026312865,0.00022215673,0.0014572061,0.001188138,0.00034469194,0.0009633073,0.0006934065,0.0008601373,0.0005220661],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.008528872,0.00052128365,0.5639785,0.0019159875,0.0046534436,0.00027764463,0.0017159965,0.11756085,0.0086396765,0.0076362477,0.0085590985,0.27601245],"study_design_scores_gemma":[0.00034652618,0.009517006,0.5438833,0.0005334635,0.002398452,0.0009211474,0.0009155017,0.37630543,0.014647723,0.00794589,0.042286795,0.0002988294],"about_ca_topic_score_codex":0.004415994,"about_ca_topic_score_gemma":0.0040507447,"teacher_disagreement_score":0.017392607,"about_ca_system_score_codex":0.000972436,"about_ca_system_score_gemma":0.0009905456,"threshold_uncertainty_score":0.09198201},"labels":[],"label_agreement":null},{"id":"W4406216116","doi":"10.3390/biomedinformatics5010004","title":"Validation of an Upgraded Virtual Reality Platform Designed for Real-Time Dialogical Psychotherapies","year":2025,"lang":"en","type":"article","venue":"BioMedInformatics","topic":"Virtual Reality Applications and Impacts","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut national de psychiatrie légale Philippe-Pinel; Université de Montréal; McGill University; Institut Universitaire en Santé Mentale de Québec","funders":"Natural Sciences and Engineering Research Council of Canada; Eli Lilly Canada; Eli Lilly and Company","keywords":"Dialogical self; Virtual reality; Human–computer interaction; Computer science; Psychology; Social psychology","score_opus":0.045161814589460406,"score_gpt":0.32548734123424145,"score_spread":0.28032552664478105,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406216116","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9458748,0.00027182966,0.049240436,0.000104568455,0.00023549092,0.0016450308,0.00033806174,0.00033501978,0.0019548624],"genre_scores_gemma":[0.92765576,0.00025018628,0.068051375,0.00009549891,0.00004157224,0.0016881874,0.00046081396,0.00006689329,0.0016897005],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99851066,0.00073225814,0.00008231355,0.00020186264,0.0003495906,0.00012323093],"domain_scores_gemma":[0.9979976,0.0008791306,0.00014172436,0.00030051012,0.0004357633,0.0002452374],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027795224,0.0006578975,0.00041888052,0.00053079403,0.00027537308,0.00067956274,0.0012572249,0.0007212135,0.003337646],"category_scores_gemma":[0.006651861,0.00026085833,0.00057519006,0.00015722294,0.0006039295,0.000531085,0.001395911,0.00054762955,0.00061360607],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.011589109,0.012781783,0.016852291,0.0026199417,0.0003449545,0.0014799086,0.00922746,0.014039234,0.60858136,0.0032004947,0.0031234361,0.3161599],"study_design_scores_gemma":[0.006405173,0.20705605,0.1865143,0.0012425391,0.0016813488,0.0066707185,0.007990851,0.11153804,0.38997674,0.0035593796,0.07650576,0.00085911865],"about_ca_topic_score_codex":0.00049604964,"about_ca_topic_score_gemma":0.00035354847,"teacher_disagreement_score":0.003337646,"about_ca_system_score_codex":0.0002521901,"about_ca_system_score_gemma":0.00060348446,"threshold_uncertainty_score":0.014699757},"labels":[],"label_agreement":null},{"id":"W4410004216","doi":"10.3390/biomedinformatics5020023","title":"Escalate Prognosis of Parkinson’s Disease Employing Wavelet Features and Artificial Intelligence from Vowel Phonation","year":2025,"lang":"en","type":"article","venue":"BioMedInformatics","topic":"Voice and Speech Disorders","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Phonation; Vowel; Wavelet; Speech recognition; Parkinson's disease; Audiology; Artificial intelligence; Disease; Medicine; Computer science; Pathology","score_opus":0.020786354038577384,"score_gpt":0.28767050301848734,"score_spread":0.26688414897991,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410004216","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6734186,0.0032356472,0.31593502,0.0008878783,0.00024093466,0.00018560939,0.0007251145,0.00082494825,0.0045463336],"genre_scores_gemma":[0.9387604,0.00078913127,0.058724906,0.00006223553,0.000093022965,0.00005134522,0.0005292568,0.000030047702,0.0009597331],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99977165,0.00003171913,0.00003062209,0.000049513328,0.000091062786,0.000025463492],"domain_scores_gemma":[0.9994159,0.00022859841,0.000101231184,0.000034009958,0.00018703587,0.0000332039],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005957743,0.00046568754,0.0004731897,0.0016108978,0.00014956076,0.00087991264,0.00027581217,0.0005482276,0.0007369944],"category_scores_gemma":[0.0018837014,0.00012458136,0.0006299882,0.0006817728,0.00020849289,0.0006382512,0.0003237493,0.00042932676,0.00037978875],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007916977,0.00030821064,0.08850249,0.00028583987,0.0002153015,0.0006729719,0.00017332031,0.04699517,0.045619544,0.0012112771,0.0024541407,0.81277007],"study_design_scores_gemma":[0.000030772415,0.00083564495,0.11194596,0.00011240226,0.00019521738,0.0014638316,0.00021301559,0.8570424,0.02138332,0.0038784626,0.0028146028,0.00008436858],"about_ca_topic_score_codex":0.0007744281,"about_ca_topic_score_gemma":0.0007812846,"teacher_disagreement_score":0.0016108978,"about_ca_system_score_codex":0.00027541668,"about_ca_system_score_gemma":0.00027521956,"threshold_uncertainty_score":0.0031508207},"labels":[],"label_agreement":null},{"id":"W4413780474","doi":"10.3390/biomedinformatics5030048","title":"Real-Time Applications of Biophysiological Markers in Virtual-Reality Exposure Therapy: A Systematic Review","year":2025,"lang":"en","type":"review","venue":"BioMedInformatics","topic":"Virtual Reality Applications and Impacts","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut national de psychiatrie légale Philippe-Pinel; Université de Montréal; Institut universitaire en santé mentale de Montréal","funders":"","keywords":"Exposure therapy; Virtual reality; Computer science; Psychology; Human–computer interaction","score_opus":0.03798947096994316,"score_gpt":0.33280067868186797,"score_spread":0.2948112077119248,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413780474","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00015118084,0.9993761,0.00009336322,0.000079185265,0.00004445209,0.00003346079,0.00006601378,0.000003823998,0.00015241193],"genre_scores_gemma":[0.0015735,0.9979086,0.00021965787,0.00010724286,0.000027764429,0.00005262255,0.00005742618,0.000001785867,0.000051370025],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.99843484,0.00042665965,0.0005814697,0.00015232501,0.00035006332,0.000054737156],"domain_scores_gemma":[0.9917951,0.0063065835,0.0010052613,0.000116819654,0.0006704887,0.000105705374],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002628748,0.0012596941,0.004199902,0.0076988735,0.00043756547,0.0018595002,0.0015799769,0.001425894,0.005277898],"category_scores_gemma":[0.010595019,0.00054646295,0.0038790945,0.007195696,0.0005461281,0.0015419077,0.0011222118,0.00087080745,0.00054519635],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013109267,0.000031261075,0.0003102876,0.7778861,0.0023762076,0.0000965502,0.00012553167,0.00016059267,0.00024580696,0.00044049692,0.0029081504,0.21528786],"study_design_scores_gemma":[0.00015986213,0.0002738088,0.004021464,0.83881485,0.031206176,0.00096889137,0.00029286862,0.00017346283,0.00040591587,0.0008912185,0.1227303,0.00006123366],"about_ca_topic_score_codex":0.003947634,"about_ca_topic_score_gemma":0.009727689,"teacher_disagreement_score":0.0076988735,"about_ca_system_score_codex":0.0012377347,"about_ca_system_score_gemma":0.0058217063,"threshold_uncertainty_score":0.017656326},"labels":[],"label_agreement":null},{"id":"W4413981801","doi":"10.3390/biomedinformatics5030051","title":"Quantum-Enhanced Dual-Backbone Architecture for Accurate Gastrointestinal Disease Detection Using Endoscopic Imaging","year":2025,"lang":"en","type":"article","venue":"BioMedInformatics","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"Natural Sciences and Engineering Research Council of Canada; Université de Moncton","keywords":"Dual (grammatical number); Architecture; Computer science; Quantum; Medicine; Artificial intelligence; Computer vision; Physics; Philosophy; History; Quantum mechanics","score_opus":0.012015752937972588,"score_gpt":0.265816544482688,"score_spread":0.2538007915447154,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413981801","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17165309,0.0007963313,0.82000077,0.0008210418,0.00006835235,0.000053668235,0.00014103846,0.0012029572,0.0052627334],"genre_scores_gemma":[0.9103165,0.00022502587,0.08711502,0.0001928434,0.00002044577,0.000040626615,0.00012575657,0.000028526934,0.0019352175],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99991024,0.000021878095,0.0000032083592,0.00002311149,0.000026570586,0.000015002748],"domain_scores_gemma":[0.9998259,0.00005946043,0.000023154786,0.000026951911,0.00004801317,0.000016513219],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043236392,0.0002624957,0.0002769695,0.0002374919,0.00022056155,0.00034466814,0.0008843758,0.0005247206,0.0016829041],"category_scores_gemma":[0.0008848386,0.00014729757,0.0002281231,0.00021330205,0.00047432678,0.0008839626,0.0005876018,0.00046275815,0.0003004158],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033328967,0.000253737,0.0047943755,0.00019867103,0.00010424742,0.00018602253,0.00010500525,0.6188113,0.06471022,0.027597656,0.005174951,0.27773052],"study_design_scores_gemma":[0.000004528057,0.000029685856,0.0001879275,0.0000024485903,0.0000067679543,0.000017378587,0.0000032772446,0.99399865,0.0026192367,0.0026981803,0.00042895504,0.0000030480348],"about_ca_topic_score_codex":0.0021087574,"about_ca_topic_score_gemma":0.003206506,"teacher_disagreement_score":0.0021087574,"about_ca_system_score_codex":0.0006993799,"about_ca_system_score_gemma":0.00068214396,"threshold_uncertainty_score":0.005629897},"labels":[],"label_agreement":null},{"id":"W4414156394","doi":"10.3390/biomedinformatics5030053","title":"High-Precision, Automatic, and Fast Segmentation Method of Hepatic Vessels and Liver Tumors from CT Images Using a Fusion Decision-Based Stacking Deep Learning Model","year":2025,"lang":"en","type":"article","venue":"BioMedInformatics","topic":"AI in cancer detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Outaouais","funders":"","keywords":"Deep learning; Segmentation; Robustness (evolution); Stacking; Liver tumor; Flexibility (engineering); Image segmentation; Pattern recognition (psychology)","score_opus":0.013303542469609905,"score_gpt":0.2859272044504051,"score_spread":0.2726236619807952,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414156394","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07144167,0.0005015798,0.92418396,0.00032427115,0.000055872373,0.0000778603,0.00017140649,0.0017995156,0.0014437721],"genre_scores_gemma":[0.7353713,0.0003127076,0.26006913,0.00030543195,0.00007179075,0.00012966493,0.0007040861,0.0001305094,0.0029054359],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995079,0.00006363396,0.000034501303,0.00016448855,0.00015045669,0.00007894098],"domain_scores_gemma":[0.9994861,0.0001587528,0.00007733939,0.0000615591,0.00017562263,0.000040674422],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008751875,0.00094065565,0.00080075057,0.0010094537,0.00037908685,0.0008772564,0.0011003724,0.0010686818,0.00095795724],"category_scores_gemma":[0.001354748,0.00044683405,0.0011950299,0.00059035444,0.00041682753,0.00085379055,0.00095090753,0.0011264714,0.00044747582],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028694022,0.00021811118,0.0066377125,0.000102825914,0.00018490272,0.00023879428,0.00012552705,0.4861658,0.029494945,0.0028138107,0.0038863888,0.46984422],"study_design_scores_gemma":[0.000004853221,0.00003890563,0.00047067934,0.0000057506822,0.000025968913,0.000038740793,0.0000071073373,0.9926899,0.0051764785,0.0011587156,0.0003737304,0.000009114259],"about_ca_topic_score_codex":0.005765246,"about_ca_topic_score_gemma":0.005572408,"teacher_disagreement_score":0.005765246,"about_ca_system_score_codex":0.000889818,"about_ca_system_score_gemma":0.0015289992,"threshold_uncertainty_score":0.011463404},"labels":[],"label_agreement":null}]}