{"meta":{"query_hash":"7972f8a084ed","filters":{"venue":"Healthcare Analytics"},"cohort_total":14,"direct_labels_cover":0,"predictions_cover":14,"exported":14,"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/7972f8a084ed","api":"https://metacan.xera.ac/api/v1/cohort?venue=Healthcare+Analytics"},"results":[{"id":"W4223998916","doi":"10.1016/j.health.2022.100048","title":"Modeling location–allocation of emergency medical service stations and ambulance routing problems considering the variability of events and recurrent traffic congestion: A real case study","year":2022,"lang":"en","type":"article","venue":"Healthcare Analytics","topic":"Facility Location and Emergency Management","field":"Business, Management and Accounting","cited_by":26,"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 Calgary; Western University","funders":"","keywords":"Computer science; Probabilistic logic; Heuristic; Service (business); Routing (electronic design automation); Emergency vehicle; Emergency medical services; Operations research; Ambulance service; Traffic congestion; Vehicle routing problem; Transport engineering; Real-time computing; Computer network; Medical emergency; Engineering; Medicine; Artificial intelligence; Business","score_opus":0.0888919836709489,"score_gpt":0.32349667229509665,"score_spread":0.23460468862414774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4223998916","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":[0.7676572,0.0010202804,0.21916208,0.0014642352,0.00018266497,0.00031374532,0.00074219046,0.0002882571,0.009169271],"genre_scores_gemma":[0.97941005,0.0003229075,0.017007481,0.00004333556,0.000049356524,0.00014740914,0.0002794569,0.000027680693,0.0027123385],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99878806,0.0005166576,0.00004564436,0.00022550725,0.00014277562,0.00028139522],"domain_scores_gemma":[0.99683315,0.0022069607,0.00039029447,0.00010814907,0.00028268778,0.00017879876],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019973866,0.0015937149,0.0014003749,0.00094581273,0.00089257053,0.0017378648,0.00217346,0.0033168287,0.0021464028],"category_scores_gemma":[0.0030769666,0.00087118725,0.0017332961,0.0015457489,0.0009837549,0.0013995623,0.0010218356,0.0017434866,0.00016875105],"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":[0.00002253723,0.00006353842,0.00076026865,0.000018792887,0.000018865432,0.00019172071,0.000015708905,0.99670225,0.00015311774,0.0008810074,0.00018313213,0.0009891664],"study_design_scores_gemma":[0.0000062539634,0.00001884651,0.00022660103,0.0000014194035,0.0000071437225,0.00001618843,0.00003219408,0.99923646,0.000059560753,0.00031286065,0.000078631514,0.000003773355],"about_ca_topic_score_codex":0.031998053,"about_ca_topic_score_gemma":0.01906168,"teacher_disagreement_score":0.031998053,"about_ca_system_score_codex":0.002358461,"about_ca_system_score_gemma":0.001784314,"threshold_uncertainty_score":0.06362361},"labels":[],"label_agreement":null},{"id":"W4281742449","doi":"10.1016/j.health.2022.100068","title":"A COVID-19 Search Engine (CO-SE) with Transformer-based architecture","year":2022,"lang":"en","type":"article","venue":"Healthcare Analytics","topic":"Topic Modeling","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":"Public Health Ontario; University of Toronto","funders":"","keywords":"Generalizability theory; Computer science; Coronavirus disease 2019 (COVID-19); Information retrieval; Transformer; Benchmark (surveying); Question answering; Natural language processing; Artificial intelligence; Infectious disease (medical specialty); Disease","score_opus":0.0826462659495686,"score_gpt":0.3513920236641443,"score_spread":0.2687457577145757,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281742449","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":[0.099184476,0.0059281085,0.74932545,0.0018590649,0.00041969115,0.0018761758,0.012186622,0.11153754,0.017682822],"genre_scores_gemma":[0.4156644,0.002276156,0.52326816,0.0009985355,0.00020062855,0.00070968684,0.036910884,0.0010347107,0.018936882],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993104,0.000094835006,0.00007726623,0.0002321634,0.00019741445,0.000087872315],"domain_scores_gemma":[0.99936,0.00019214506,0.00003607752,0.00011143785,0.00023423122,0.00006606486],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011164342,0.0010628063,0.0012908574,0.0039042337,0.00050856016,0.001290989,0.0019902997,0.0014256183,0.004504025],"category_scores_gemma":[0.0026309632,0.00045453664,0.0012166892,0.0030795375,0.0003578267,0.0029476085,0.0014307867,0.00068338186,0.00451003],"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":[0.0018529693,0.0012399154,0.015590757,0.0019619004,0.0005931289,0.0007971632,0.00047433746,0.059851717,0.04268197,0.02046091,0.10324873,0.7512465],"study_design_scores_gemma":[0.00017074482,0.0004712694,0.0026369432,0.000051593845,0.00018983673,0.0007598294,0.00012785051,0.9360723,0.019480506,0.0077540153,0.03219467,0.00009044015],"about_ca_topic_score_codex":0.016895356,"about_ca_topic_score_gemma":0.022033533,"teacher_disagreement_score":0.016895356,"about_ca_system_score_codex":0.0010494225,"about_ca_system_score_gemma":0.0019604408,"threshold_uncertainty_score":0.033594012},"labels":[],"label_agreement":null},{"id":"W4293393965","doi":"10.1016/j.health.2022.100100","title":"A machine learning model for predicting, diagnosing, and mitigating health disparities in hospital readmission","year":2022,"lang":"en","type":"article","venue":"Healthcare Analytics","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University; University of Toronto","funders":"","keywords":"Pipeline (software); Computer science; Machine learning; Process (computing); Artificial intelligence; Data collection; Data mining; Statistics","score_opus":0.028984116587703574,"score_gpt":0.316319202835405,"score_spread":0.28733508624770143,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293393965","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":[0.25431865,0.0010547743,0.73242325,0.006084104,0.00025465406,0.00035225344,0.0014177292,0.0010416107,0.0030529273],"genre_scores_gemma":[0.8951312,0.00030433421,0.09976858,0.000608043,0.00021458582,0.00030309905,0.00092507625,0.00003611514,0.0027090292],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988367,0.0004879237,0.00007594408,0.00027747444,0.00021801204,0.00010395402],"domain_scores_gemma":[0.9943299,0.0041890424,0.0004863354,0.00026158654,0.00059203355,0.00014097708],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005400137,0.00066515186,0.000816872,0.0009471655,0.0007152208,0.0012293514,0.0013184701,0.0012308921,0.0014586631],"category_scores_gemma":[0.013287198,0.0002807747,0.0006915857,0.0007034654,0.0005163892,0.0013958034,0.00088733435,0.0017429746,0.00027632964],"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":[0.00043754847,0.00045969605,0.028182514,0.00007845164,0.00012777947,0.0001297276,0.00014128826,0.86468506,0.000881876,0.0075445194,0.0039701527,0.0933614],"study_design_scores_gemma":[0.00001008646,0.000029724313,0.00083196355,0.000006987526,0.000008531368,0.000009639843,0.00000711608,0.9951573,0.0001801349,0.0035818703,0.00017185757,0.000004794489],"about_ca_topic_score_codex":0.011884611,"about_ca_topic_score_gemma":0.009085951,"teacher_disagreement_score":0.011884611,"about_ca_system_score_codex":0.001415273,"about_ca_system_score_gemma":0.0022487522,"threshold_uncertainty_score":0.02855897},"labels":[],"label_agreement":null},{"id":"W4323269354","doi":"10.1016/j.health.2023.100155","title":"A framework for implementing machine learning in healthcare based on the concepts of preconditions and postconditions","year":2023,"lang":"en","type":"article","venue":"Healthcare Analytics","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ottawa Hospital; Nova Scotia Health Authority; Dalhousie University","funders":"Faculty of Graduate Studies, Dalhousie University; Dalhousie University","keywords":"Bridge (graph theory); Computer science; Health care; Software engineering; Software; Perception; Artificial intelligence; Machine learning; Knowledge management; Risk analysis (engineering); Programming language; Business","score_opus":0.24686896338909978,"score_gpt":0.5001629631633152,"score_spread":0.2532939997742154,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323269354","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":[0.0007474149,0.00008040135,0.99431956,0.0006256026,0.00007113545,0.00024025142,0.000055797118,0.0012315193,0.0026282822],"genre_scores_gemma":[0.037475545,0.0002969276,0.95859617,0.0004924104,0.00011857417,0.00076947163,0.00019926547,0.0002714516,0.0017802503],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.991458,0.0035993645,0.0011795989,0.0011872796,0.0019255164,0.0006502564],"domain_scores_gemma":[0.9878642,0.007910819,0.00091484416,0.0014833619,0.001349773,0.00047686344],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014889728,0.0014442069,0.0009200506,0.0019445932,0.0016687766,0.0060952012,0.0038411133,0.0032013403,0.007410337],"category_scores_gemma":[0.018018752,0.0013792969,0.0032873158,0.0009717553,0.0082445815,0.007117283,0.004588888,0.005868131,0.0022449892],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008438218,0.00012811623,0.00080769765,0.00044998017,0.000058088022,0.0006841632,0.001601401,0.034368016,0.0029311222,0.9118116,0.0032492916,0.043826155],"study_design_scores_gemma":[0.0001612889,0.00022204356,0.00031718926,0.00068448717,0.00013511247,0.00053256744,0.0003622334,0.17783739,0.0099364,0.70109075,0.108562954,0.00015754199],"about_ca_topic_score_codex":0.007056358,"about_ca_topic_score_gemma":0.005835566,"teacher_disagreement_score":0.014889728,"about_ca_system_score_codex":0.0022882377,"about_ca_system_score_gemma":0.007734697,"threshold_uncertainty_score":0.078745365},"labels":[],"label_agreement":null},{"id":"W4378188551","doi":"10.1016/j.health.2023.100197","title":"A simulation model for predicting hospital occupancy for Covid-19 using archetype analysis","year":2023,"lang":"en","type":"article","venue":"Healthcare Analytics","topic":"Healthcare Operations and Scheduling Optimization","field":"Health Professions","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal; Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Occupancy; Archetype; Coronavirus disease 2019 (COVID-19); Computer science; Medicine; Engineering; Internal medicine; Art","score_opus":0.38647997586468197,"score_gpt":0.5630579236758887,"score_spread":0.17657794781120673,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378188551","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":[0.46786538,0.00046846285,0.50646085,0.0020754377,0.00016717278,0.0003102694,0.003850526,0.0009361017,0.017865777],"genre_scores_gemma":[0.97000384,0.00022565329,0.024701867,0.00007819944,0.000027326987,0.00022786099,0.0010627642,0.000035266585,0.0036372538],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995591,0.0001734557,0.000029481604,0.000087510365,0.000059764538,0.00009065588],"domain_scores_gemma":[0.99827325,0.0011705795,0.0001718225,0.0000581413,0.00019192256,0.000134267],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010366989,0.00075487274,0.0007600304,0.0009315995,0.0006814562,0.0012409413,0.0013288264,0.0015375745,0.004220525],"category_scores_gemma":[0.003203791,0.0004647685,0.0009862708,0.00079003006,0.00065074617,0.0006844521,0.0009566315,0.0012159889,0.00036737672],"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":[0.000019428462,0.0000118854905,0.0007978073,0.0000058466376,0.000004926265,0.000021784757,0.00001481887,0.99711716,0.000060400387,0.0012769202,0.00010536496,0.0005636014],"study_design_scores_gemma":[0.000004180574,0.000006059181,0.000097387434,0.0000015649875,0.0000019569268,0.0000031221448,0.000007848046,0.9992995,0.000022392509,0.0004650095,0.00008874459,0.0000021560402],"about_ca_topic_score_codex":0.047532696,"about_ca_topic_score_gemma":0.019424362,"teacher_disagreement_score":0.047532696,"about_ca_system_score_codex":0.0017278681,"about_ca_system_score_gemma":0.002041314,"threshold_uncertainty_score":0.094512045},"labels":[],"label_agreement":null},{"id":"W4378904522","doi":"10.1016/j.health.2023.100202","title":"Development and performance analysis of machine learning methods for predicting depression among menopausal women","year":2023,"lang":"en","type":"article","venue":"Healthcare Analytics","topic":"Menopause: Health Impacts and Treatments","field":"Medicine","cited_by":18,"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","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Receiver operating characteristic; Recall; Artificial intelligence; Machine learning; Depression (economics); Random forest; Menopause; Computer science; Psychology; Medicine; Cognitive psychology","score_opus":0.07805832662635322,"score_gpt":0.41709164268224524,"score_spread":0.33903331605589204,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378904522","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":[0.7537912,0.008488844,0.22487542,0.0011100175,0.00034802232,0.0005969934,0.002222695,0.0026382594,0.005928514],"genre_scores_gemma":[0.88614583,0.0010619626,0.10985516,0.00009832902,0.00008995632,0.00025456396,0.00151745,0.000037143203,0.0009396102],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997776,0.00088662084,0.00023988321,0.0002800216,0.00068655994,0.00013093669],"domain_scores_gemma":[0.9879198,0.008627189,0.00062522636,0.00038726153,0.0022971604,0.00014322996],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006894276,0.0007982475,0.0007831749,0.002855923,0.00034283524,0.0010812449,0.00065972365,0.0007722942,0.000611303],"category_scores_gemma":[0.015644347,0.00020477598,0.00067910727,0.0012980093,0.00015681075,0.00087700604,0.0003342349,0.00080606906,0.0003866756],"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":[0.0008446021,0.0012115779,0.22155091,0.0004637548,0.0005824435,0.00021039824,0.00020101036,0.15296118,0.0065609026,0.001453416,0.005878285,0.60808146],"study_design_scores_gemma":[0.000031036823,0.00049191294,0.042158518,0.00008033942,0.000098485376,0.00015532968,0.00011064109,0.9502253,0.0045648646,0.0007235701,0.0013345425,0.000025451698],"about_ca_topic_score_codex":0.005870206,"about_ca_topic_score_gemma":0.0036972081,"teacher_disagreement_score":0.006894276,"about_ca_system_score_codex":0.00064231013,"about_ca_system_score_gemma":0.001038134,"threshold_uncertainty_score":0.036460876},"labels":[],"label_agreement":null},{"id":"W4386805057","doi":"10.1016/j.health.2023.100259","title":"A comprehensive review of artificial intelligence methods and applications in skin cancer diagnosis and treatment: Emerging trends and challenges","year":2023,"lang":"en","type":"review","venue":"Healthcare Analytics","topic":"Cutaneous Melanoma Detection and Management","field":"Medicine","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Triage; Deep learning; Skin cancer; Computer science; Machine learning; Data science; Medicine; Cancer","score_opus":0.32663495605918,"score_gpt":0.5117766881800121,"score_spread":0.18514173212083207,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386805057","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":[0.000109018976,0.9980913,0.00035173993,0.0004450637,0.00016783757,0.000009852064,0.000045533157,0.000011243557,0.00076844567],"genre_scores_gemma":[0.00074229523,0.997865,0.0005962437,0.00029490257,0.00017984827,0.000013131848,0.0000610189,0.0000034852958,0.00024420064],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992859,0.0001911439,0.00016590473,0.0000913678,0.00022995968,0.000035755824],"domain_scores_gemma":[0.9965983,0.0025613278,0.00023492456,0.0000644407,0.00047592667,0.00006509487],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015015734,0.00081427663,0.001418747,0.003892877,0.0002995855,0.001322933,0.0007985811,0.0011618613,0.0045023235],"category_scores_gemma":[0.0041148253,0.00035660516,0.0012491709,0.004205899,0.00041337518,0.0015800942,0.00059569394,0.0013883051,0.0015991712],"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":[0.00005084211,0.00006290622,0.00035396212,0.0634284,0.00026092687,0.0001207131,0.00010629603,0.00058465806,0.0007700977,0.0038896108,0.031029338,0.8993423],"study_design_scores_gemma":[0.000019280018,0.00014594608,0.0016163565,0.029079821,0.00055605464,0.00084788364,0.00011557105,0.0004330467,0.0005508444,0.00411976,0.96246886,0.00004666835],"about_ca_topic_score_codex":0.0018755452,"about_ca_topic_score_gemma":0.002995676,"teacher_disagreement_score":0.0045023235,"about_ca_system_score_codex":0.00069414306,"about_ca_system_score_gemma":0.002365712,"threshold_uncertainty_score":0.015061796},"labels":[],"label_agreement":null},{"id":"W4388142552","doi":"10.1016/j.health.2023.100275","title":"A deterministic compartmental model for investigating the impact of escapees on the transmission dynamics of COVID-19","year":2023,"lang":"en","type":"article","venue":"Healthcare Analytics","topic":"COVID-19 epidemiological studies","field":"Mathematics","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":"York University","funders":"","keywords":"Pandemic; Coronavirus disease 2019 (COVID-19); Econometrics; Transmission (telecommunications); Quarantine; Outbreak; Statistics; Mathematics; Demographic economics; Economics; Development economics; Computer science; Biology; Medicine; Disease; Virology; Infectious disease (medical specialty)","score_opus":0.5456030815934967,"score_gpt":0.5345263395335692,"score_spread":0.01107674205992748,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388142552","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":[0.31160694,0.0021200564,0.65512055,0.0025580933,0.00036886722,0.00029514896,0.0027385156,0.00033632846,0.024855522],"genre_scores_gemma":[0.96190345,0.0009175815,0.021800503,0.00020444908,0.00007596664,0.0003620739,0.00070280617,0.000037927057,0.013995363],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999398,0.0002453816,0.000033810207,0.00013309548,0.00007046558,0.000119338794],"domain_scores_gemma":[0.99774134,0.0014765598,0.00033066134,0.0000613153,0.0003025324,0.000087542365],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011531662,0.0010477663,0.0011090571,0.000808832,0.0006250836,0.0014558135,0.0016170748,0.0019463078,0.0037093456],"category_scores_gemma":[0.0041567134,0.0005230428,0.0015889276,0.0007128164,0.00086422154,0.0009702446,0.0013743378,0.0016495545,0.00033246732],"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":[0.000044735927,0.000025068008,0.0015580066,0.000054887925,0.000046873778,0.00014868169,0.00005411675,0.98498744,0.00075872906,0.011082222,0.00029831336,0.00094100076],"study_design_scores_gemma":[0.000008053221,0.000018304025,0.00029319248,0.000005958927,0.000021664238,0.000015742457,0.000024267401,0.99745935,0.00008595454,0.0017249783,0.0003329322,0.000009670356],"about_ca_topic_score_codex":0.033456713,"about_ca_topic_score_gemma":0.014176498,"teacher_disagreement_score":0.033456713,"about_ca_system_score_codex":0.0016655108,"about_ca_system_score_gemma":0.001572695,"threshold_uncertainty_score":0.06652397},"labels":[],"label_agreement":null},{"id":"W4390929460","doi":"10.1016/j.health.2024.100302","title":"A novel fractional-order stochastic epidemic model to analyze the role of media awareness in the spread of conjunctivitis","year":2024,"lang":"en","type":"article","venue":"Healthcare Analytics","topic":"Mathematical and Theoretical Epidemiology and Ecology Models","field":"Medicine","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Randomness; Epidemic model; Basic reproduction number; Hopf bifurcation; Order (exchange); Pandemic; Disease; Outbreak; Computer science; Transmission (telecommunications); Disease transmission; Stability (learning theory); Bifurcation; Econometrics; Mathematics; Infectious disease (medical specialty); Coronavirus disease 2019 (COVID-19); Medicine; Economics; Environmental health; Statistics; Telecommunications; Virology; Physics; Machine learning; Nonlinear system; Pathology","score_opus":0.0646368250596077,"score_gpt":0.37071073111132874,"score_spread":0.306073906051721,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390929460","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":[0.14136626,0.001278141,0.8320757,0.002891628,0.0004334777,0.000101210724,0.0005490447,0.00017798305,0.021126634],"genre_scores_gemma":[0.95812035,0.0010058039,0.026286315,0.00031548282,0.00024409217,0.000108399865,0.00016991842,0.000029372308,0.013720302],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99964595,0.00010420139,0.000018688694,0.000070675436,0.00007517042,0.00008526816],"domain_scores_gemma":[0.9992679,0.00035648103,0.00018292577,0.00003196423,0.000097180615,0.000063504354],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060012017,0.0007677045,0.00077172444,0.00092446565,0.0006790943,0.0014388579,0.0013106151,0.0018773797,0.0016713422],"category_scores_gemma":[0.0018992833,0.0002964595,0.0012068339,0.00070039264,0.00078280934,0.0012796585,0.0010181109,0.0010099025,0.00017361596],"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":[0.000052418356,0.000102527636,0.002928697,0.00009923142,0.00008768366,0.0006670996,0.00023643652,0.79847556,0.0053686993,0.18344867,0.0016801844,0.0068527567],"study_design_scores_gemma":[0.0000070991096,0.000017447004,0.0002307889,0.0000056135455,0.000018507388,0.0000681253,0.000026279977,0.98696333,0.00012202638,0.011753464,0.00077711436,0.000010148389],"about_ca_topic_score_codex":0.008766723,"about_ca_topic_score_gemma":0.003893167,"teacher_disagreement_score":0.008766723,"about_ca_system_score_codex":0.0011251668,"about_ca_system_score_gemma":0.0012051786,"threshold_uncertainty_score":0.017431378},"labels":[],"label_agreement":null},{"id":"W4402642543","doi":"10.1016/j.health.2024.100364","title":"A data envelopment analysis model for optimizing transfer time of ischemic stroke patients under endovascular thrombectomy","year":2024,"lang":"en","type":"article","venue":"Healthcare Analytics","topic":"Acute Ischemic Stroke Management","field":"Medicine","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":"Dalhousie University","funders":"","keywords":"Ischemic stroke; Endovascular treatment; Envelopment; Medicine; Stroke (engine); Computer science; Cardiology; Internal medicine; Data envelopment analysis; Radiology; Engineering; Ischemia; Statistics; Mathematics; Mechanical engineering","score_opus":0.06634451672134503,"score_gpt":0.3300206066781757,"score_spread":0.2636760899568307,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402642543","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":[0.31387144,0.0012315799,0.67148656,0.0012704454,0.0000960484,0.00062483293,0.0022993577,0.0004382,0.008681522],"genre_scores_gemma":[0.9395957,0.00041906754,0.056374885,0.0000723906,0.000019145924,0.00047373353,0.00086439366,0.000049908493,0.0021307836],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983246,0.0009683463,0.00007954222,0.00023197729,0.00014523955,0.00025044815],"domain_scores_gemma":[0.99529946,0.0036472355,0.00028665207,0.00009156892,0.00052731956,0.00014774861],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038771825,0.0015564745,0.001985406,0.0015736665,0.00060605636,0.002237477,0.0010458805,0.0013709521,0.0026279232],"category_scores_gemma":[0.007207266,0.00084407435,0.0015413498,0.0013762062,0.0005828114,0.0007845597,0.0013691827,0.0014669081,0.0002265162],"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":[0.000041179468,0.000018603218,0.000652822,0.000030543437,0.000031125423,0.000024726409,0.000025808755,0.9962322,0.00007530295,0.0010455074,0.00012995013,0.0016923666],"study_design_scores_gemma":[0.000008525741,0.000019074047,0.0002696191,0.00000843011,0.000009786002,0.0000031890036,0.000020879292,0.99879515,0.00005810361,0.00066387677,0.0001391323,0.0000042097863],"about_ca_topic_score_codex":0.06629919,"about_ca_topic_score_gemma":0.02317152,"teacher_disagreement_score":0.06629919,"about_ca_system_score_codex":0.0038227306,"about_ca_system_score_gemma":0.0054681567,"threshold_uncertainty_score":0.13182658},"labels":[],"label_agreement":null},{"id":"W4408312721","doi":"10.1016/j.health.2025.100390","title":"A comparative study of explainable machine learning models with Shapley values for diabetes prediction","year":2025,"lang":"en","type":"article","venue":"Healthcare Analytics","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Diabetes mellitus; Computer science; Machine learning; Artificial intelligence; Econometrics; Medicine; Mathematics; Endocrinology","score_opus":0.07048604247240084,"score_gpt":0.34299081406969206,"score_spread":0.27250477159729125,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408312721","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":[0.46948066,0.005707618,0.51272666,0.0038188638,0.00020874412,0.00015072415,0.0005948836,0.0004920131,0.006819769],"genre_scores_gemma":[0.9638826,0.0010559956,0.03333403,0.00020152511,0.00013329336,0.000062000116,0.000399026,0.000037993603,0.00089349673],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99625313,0.0025411805,0.00016831799,0.00041937857,0.00044574973,0.00017228094],"domain_scores_gemma":[0.9278048,0.06508848,0.0024320993,0.001965194,0.0020307265,0.0006786491],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0121815605,0.0012839522,0.001625278,0.0021561095,0.0006584376,0.0021411616,0.0014689896,0.0013605944,0.0020162428],"category_scores_gemma":[0.040726252,0.00040747097,0.0016792406,0.0014758537,0.0014720361,0.0029265773,0.0014617862,0.0019322755,0.00016327373],"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":[0.00021672658,0.00011462085,0.0109479325,0.00015115562,0.00027530777,0.000118413474,0.0002848236,0.8935158,0.00016843388,0.06239826,0.0010796976,0.030728856],"study_design_scores_gemma":[0.000012563519,0.000062800886,0.0008659132,0.000025085461,0.000023464,0.000016163061,0.000034378587,0.95886594,0.0000704195,0.03979425,0.0002143026,0.000014651657],"about_ca_topic_score_codex":0.0050554583,"about_ca_topic_score_gemma":0.0028138917,"teacher_disagreement_score":0.0121815605,"about_ca_system_score_codex":0.001998771,"about_ca_system_score_gemma":0.0011765407,"threshold_uncertainty_score":0.064423025},"labels":[],"label_agreement":null},{"id":"W4414300886","doi":"10.1016/j.health.2025.100419","title":"An analytical review of biosensor-based chronic pain quantification in healthcare","year":2025,"lang":"en","type":"article","venue":"Healthcare Analytics","topic":"Musculoskeletal pain and rehabilitation","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Rehabilitation Institute; University of Toronto; McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; University of Liverpool; Biotechnology and Biological Sciences Research Council; McMaster University","keywords":"Chronic pain; Biomarker; Workflow; Health care; Analytics; Precision medicine; Quantitative sensory testing","score_opus":0.03942820868580988,"score_gpt":0.4076341003621826,"score_spread":0.36820589167637274,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414300886","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":[0.00030497863,0.99289614,0.002947497,0.0008659763,0.0005873134,0.00002837839,0.00007001276,0.000023151255,0.0022765305],"genre_scores_gemma":[0.0025316048,0.992592,0.0026971796,0.00066199363,0.0004911556,0.000046936868,0.00009004915,0.00000832564,0.00088073255],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.998223,0.0004851173,0.00026497117,0.00025352856,0.00068218523,0.00009115954],"domain_scores_gemma":[0.9973035,0.0016000236,0.00022174642,0.000071307506,0.0007542107,0.000049194692],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023987598,0.0014214795,0.0017047509,0.003550373,0.00043214756,0.0019730332,0.0013556288,0.0020458973,0.003563352],"category_scores_gemma":[0.0038164458,0.00068745075,0.0012600932,0.0039465763,0.0006480328,0.0020305067,0.0010437549,0.0014827006,0.0022082615],"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":[0.00012836055,0.00013154322,0.0006242471,0.059055373,0.00030820887,0.00033314805,0.00018654774,0.0017271368,0.008332599,0.015146609,0.038494483,0.87553173],"study_design_scores_gemma":[0.000015477988,0.00038102365,0.001568235,0.011982669,0.00032921886,0.0011825725,0.00015190746,0.0013977585,0.0058725835,0.005495709,0.9715266,0.00009615026],"about_ca_topic_score_codex":0.0023007067,"about_ca_topic_score_gemma":0.0023309437,"teacher_disagreement_score":0.003563352,"about_ca_system_score_codex":0.0013354607,"about_ca_system_score_gemma":0.00206446,"threshold_uncertainty_score":0.012686014},"labels":[],"label_agreement":null},{"id":"W4416427728","doi":"10.1016/j.health.2025.100438","title":"An investigation of treatment barriers for End-Stage Kidney Disease patients using advanced analytics","year":2025,"lang":"en","type":"article","venue":"Healthcare Analytics","topic":"Dialysis and Renal Disease Management","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":"University of Toronto","funders":"","keywords":"Leverage (statistics); Analytics; Kidney disease; Emergency department; Predictive analytics; Dialysis; Disease; Medical decision making","score_opus":0.03676623571671684,"score_gpt":0.35629505927810384,"score_spread":0.319528823561387,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416427728","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":[0.9771113,0.00028109134,0.01372453,0.0037527864,0.000037276874,0.00022371608,0.0007297375,0.000046781453,0.0040928167],"genre_scores_gemma":[0.99197996,0.00015991971,0.0070394785,0.0001681259,0.00001735408,0.00008170377,0.00034746065,0.0000110323135,0.00019502798],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9946607,0.0037174518,0.00022174613,0.0003315282,0.00060097815,0.00046768977],"domain_scores_gemma":[0.9478325,0.04350816,0.0041364473,0.0013970039,0.0018624711,0.0012634228],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007284976,0.0005136325,0.00043927957,0.0018843227,0.0006446314,0.0024581193,0.001041192,0.0006643156,0.0020553772],"category_scores_gemma":[0.039585214,0.00030961237,0.00094204536,0.0019846759,0.0008120377,0.0023741268,0.002120181,0.0019114946,0.00015326236],"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":[0.00039957417,0.001959822,0.8117019,0.0003836854,0.00032518516,0.0004144856,0.0042017708,0.11143592,0.0005049441,0.02976556,0.0033236586,0.035583463],"study_design_scores_gemma":[0.000060030903,0.0007149855,0.11596148,0.00027889197,0.0001141344,0.00018878911,0.013890638,0.84009093,0.0008778697,0.022697784,0.005020192,0.000104311956],"about_ca_topic_score_codex":0.013623046,"about_ca_topic_score_gemma":0.012956421,"teacher_disagreement_score":0.013623046,"about_ca_system_score_codex":0.0018603933,"about_ca_system_score_gemma":0.0048194123,"threshold_uncertainty_score":0.03852707},"labels":[],"label_agreement":null},{"id":"W7111222802","doi":"10.1016/j.health.2025.100443","title":"An unsupervised machine learning approach for defining surge levels in emergency medical services","year":2025,"lang":"en","type":"article","venue":"Healthcare Analytics","topic":"Emergency and Acute Care Studies","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Dartmouth General Hospital; Dalhousie University","funders":"","keywords":"Cluster analysis; Adaptability; Feature (linguistics); Health care; Set (abstract data type); Surge Capacity; Overcrowding; Hyperparameter","score_opus":0.048645400111452554,"score_gpt":0.3779782607719827,"score_spread":0.32933286066053014,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7111222802","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":[0.10536704,0.00026109273,0.8887531,0.00041025347,0.000053600423,0.00024977638,0.0007452182,0.0011017687,0.0030581786],"genre_scores_gemma":[0.7993894,0.000120834215,0.19712402,0.00018853285,0.00005089288,0.00030458215,0.0011859328,0.000052302476,0.0015835215],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99912876,0.0003306967,0.000070586335,0.00021797016,0.00018513924,0.00006688796],"domain_scores_gemma":[0.99840575,0.000941122,0.00018258556,0.00010167021,0.00033574487,0.00003317686],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012177791,0.00069844036,0.0004889323,0.0017462723,0.00041827682,0.00074515364,0.0010404909,0.00065818976,0.00057432015],"category_scores_gemma":[0.0047123404,0.00019906029,0.000545319,0.001274926,0.00034286093,0.0005896872,0.00058572384,0.00084041676,0.0002476802],"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":[0.00015654722,0.00037387485,0.026147563,0.000087687906,0.0001998391,0.00015933505,0.00025957555,0.7234548,0.002663114,0.004829657,0.0037602268,0.2379077],"study_design_scores_gemma":[0.000005145406,0.000023832672,0.0020222678,0.000008402203,0.000009671863,0.000022473629,0.000036203295,0.9944318,0.00047448123,0.0025776885,0.000380974,0.000007017044],"about_ca_topic_score_codex":0.013808289,"about_ca_topic_score_gemma":0.016389322,"teacher_disagreement_score":0.013808289,"about_ca_system_score_codex":0.0010230361,"about_ca_system_score_gemma":0.0012296594,"threshold_uncertainty_score":0.027455807},"labels":[],"label_agreement":null}]}