{"id":"W2980947956","doi":"10.5195/ijms.2019.398","title":"What Was the Name of That Drug? How Medical Students can Make the Most Out of Their Education","year":2019,"lang":"en","type":"article","venue":"International Journal of Medical Students","topic":"Innovations in Medical Education","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Recall; Foundation (evidence); Contrast (vision); Active learning (machine learning); Mathematics education; Psychology; Medical education; Computer science; Medicine; Cognitive psychology; Artificial intelligence; Political science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.006692322,0.0001974364,0.0004803747,0.0002430185,0.00006341535,0.0001925286,0.005233037,0.0002115827,0.001283323],"category_scores_gemma":[0.003324785,0.0001012952,0.0002135705,0.0002921305,0.0004850728,0.0001771128,0.0005280335,0.001112116,0.00001732097],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002774594,"about_ca_system_score_gemma":0.001768729,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007819257,"about_ca_topic_score_gemma":0.00006582923,"domain_scores_codex":[0.9796977,0.0002284676,0.001101292,0.0001942823,0.01855346,0.0002248006],"domain_scores_gemma":[0.9957823,0.0005107462,0.001152666,0.0004123216,0.00191126,0.0002307311],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003592488,0.004291378,0.5544405,0.0001262231,0.002043598,0.00004684656,0.0113733,0.000003651606,0.00014761,0.001131321,0.04557707,0.3804592],"study_design_scores_gemma":[0.01042595,0.0007689352,0.7097371,0.009863432,0.0004642908,0.001139266,0.07369813,0.0004321421,0.002294451,0.001113584,0.1896757,0.0003869762],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7794572,0.0006960714,0.0001034382,0.2068944,0.0122049,0.0003668175,0.000005067664,0.000005413855,0.0002666912],"genre_scores_gemma":[0.9843258,0.001570891,0.00006832234,0.0112172,0.001433369,0.00001590006,0.00002802763,0.0000219326,0.001318519],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3800723,"threshold_uncertainty_score":0.9996296,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0173673490977105,"score_gpt":0.3894061492932627,"score_spread":0.3720388001955522,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}