{"meta":{"query_hash":"f478795802e1","filters":{"venue":"European Journal of Radiology Artificial Intelligence"},"cohort_total":3,"direct_labels_cover":0,"predictions_cover":3,"exported":3,"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/f478795802e1","api":"https://metacan.xera.ac/api/v1/cohort?venue=European+Journal+of+Radiology+Artificial+Intelligence"},"results":[{"id":"W4414423977","doi":"10.1016/j.ejrai.2025.100042","title":"Transforming CT imaging with deep learning: Noise reduction, artifact management, and clinical applications – A comprehensive review","year":2025,"lang":"en","type":"article","venue":"European Journal of Radiology Artificial Intelligence","topic":"Medical Imaging Techniques and Applications","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 Health Network; University of Toronto; St. Michael's Hospital","funders":"GE Healthcare","keywords":"Artifact (error); Noise (video); Medical imaging; Computed tomography; Noise reduction","score_opus":0.04583740334157864,"score_gpt":0.36773667617389105,"score_spread":0.3218992728323124,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414423977","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":[0.0006422863,0.99145645,0.0055354047,0.00071350957,0.00017788797,0.000020081625,0.00004096135,0.00006154126,0.0013517641],"genre_scores_gemma":[0.004618214,0.99068516,0.0034352054,0.00032756192,0.00036229525,0.000023205002,0.000093615825,0.000019583013,0.00043523597],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99959344,0.00008169638,0.0000677571,0.000074141906,0.00015167185,0.000031249834],"domain_scores_gemma":[0.99863064,0.0009535266,0.00010149623,0.000034714027,0.00023315287,0.000046340996],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014856141,0.001148471,0.0009849421,0.0027189108,0.0002411659,0.0015680401,0.0011501313,0.0014751484,0.0023808347],"category_scores_gemma":[0.0029626514,0.0004910457,0.001032027,0.0027123955,0.0007182984,0.002008685,0.00097354146,0.0015310876,0.0010861709],"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":[0.000034199544,0.00005805744,0.00047746795,0.007495257,0.000101051875,0.000085001426,0.000052448926,0.0024922602,0.0007194678,0.0031904445,0.009447824,0.9758465],"study_design_scores_gemma":[0.000042433207,0.0005197848,0.005083824,0.018077252,0.0007220855,0.003940087,0.00023554466,0.018416442,0.0052813743,0.020264894,0.9271856,0.00023059548],"about_ca_topic_score_codex":0.002346914,"about_ca_topic_score_gemma":0.0022029467,"teacher_disagreement_score":0.0027189108,"about_ca_system_score_codex":0.00060700555,"about_ca_system_score_gemma":0.0013650073,"threshold_uncertainty_score":0.00796473},"labels":[],"label_agreement":null},{"id":"W6903070560","doi":"10.1016/j.ejrai.2025.100034","title":"Improving medical image segmentation with SAM2: analyzing the impact of object characteristics and finetuning on multi-planar datasets.","year":2025,"lang":"en","type":"article","venue":"European Journal of Radiology Artificial Intelligence","topic":"Advanced Neural Network Applications","field":"Computer Science","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":"McMaster University","funders":"","keywords":"Segmentation; Dice; Sørensen–Dice coefficient; Object (grammar); Intersection (aeronautics); Image segmentation; Pattern recognition (psychology); Object detection; Scale-space segmentation","score_opus":0.03075355265644513,"score_gpt":0.3236239499293269,"score_spread":0.2928703972728818,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6903070560","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":[0.35153458,0.011315762,0.59247684,0.0014233105,0.00051250705,0.0005013248,0.0045333547,0.032436546,0.0052657593],"genre_scores_gemma":[0.5976573,0.0020490189,0.38252726,0.0008406928,0.0001646528,0.00028147813,0.011198655,0.002384232,0.002896682],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99851495,0.00036556524,0.00012293368,0.00041016948,0.00045530402,0.00013109493],"domain_scores_gemma":[0.9971096,0.0015986267,0.00031260945,0.00040611278,0.00046167252,0.000111465175],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0048859925,0.0018813941,0.0012485838,0.0026639667,0.00067567837,0.0026275031,0.0014368814,0.0020278108,0.0016603171],"category_scores_gemma":[0.010317893,0.00071766955,0.0014452698,0.0019019763,0.0005175844,0.001573584,0.0013554669,0.0010241136,0.0013057193],"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":[0.0021776843,0.0003944377,0.038423717,0.0018635624,0.0016040296,0.00067027874,0.0006524324,0.22536269,0.12311656,0.0027144384,0.01734989,0.5856703],"study_design_scores_gemma":[0.000059680006,0.0006465116,0.014073593,0.00009502031,0.00024859884,0.0011923884,0.00020946666,0.9026973,0.06697445,0.0025331934,0.011174389,0.00009547211],"about_ca_topic_score_codex":0.004651192,"about_ca_topic_score_gemma":0.009847043,"teacher_disagreement_score":0.0048859925,"about_ca_system_score_codex":0.0008994494,"about_ca_system_score_gemma":0.0010952443,"threshold_uncertainty_score":0.025839865},"labels":[],"label_agreement":null},{"id":"W7116657578","doi":"10.1016/j.ejrai.2025.100066","title":"PARROT, an open multilingual radiology reports dataset","year":2025,"lang":"en","type":"article","venue":"European Journal of Radiology Artificial Intelligence","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","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":"McGill University Health Centre","funders":"","keywords":"Modalities; Metadata; Radiomics; Health care; Geocoding; Medical imaging","score_opus":0.2224181400231298,"score_gpt":0.47390245107519346,"score_spread":0.2514843110520637,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7116657578","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011538757,0.001504472,0.0051462804,0.0007211813,0.00021095773,0.00042477407,0.96700925,0.009913558,0.0035307163],"genre_scores_gemma":[0.009520364,0.00032628787,0.009533351,0.00019527905,0.000073749325,0.0005161054,0.97864634,0.00038853733,0.0008000792],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.993097,0.0019883162,0.001544632,0.0015041921,0.0015131925,0.0003527347],"domain_scores_gemma":[0.97923285,0.008288984,0.0036658333,0.0035341613,0.0040506264,0.0012275198],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0045186016,0.002085604,0.00074691925,0.01126611,0.0009559123,0.0025108496,0.003659683,0.002459723,0.013260891],"category_scores_gemma":[0.026570171,0.000575999,0.0014418962,0.0058050607,0.00084905507,0.002394879,0.0040381597,0.0012907849,0.017425185],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012687601,0.0003446648,0.01917236,0.009559912,0.00024653444,0.0024542299,0.0013009037,0.002879917,0.0067702,0.0031898087,0.88590896,0.066903785],"study_design_scores_gemma":[0.0005923026,0.00027771865,0.04424414,0.0013458638,0.00017635727,0.0030164584,0.001485502,0.007035217,0.005657281,0.0020528813,0.93388116,0.00023514309],"about_ca_topic_score_codex":0.012423281,"about_ca_topic_score_gemma":0.015761664,"teacher_disagreement_score":0.013260891,"about_ca_system_score_codex":0.00177674,"about_ca_system_score_gemma":0.002858114,"threshold_uncertainty_score":0.044362128},"labels":[],"label_agreement":null}]}