{"id":"W3093082404","doi":"10.1017/cjn.2020.236","title":"Don’t Make the Best of It, Make It Better: Matching to Residency Programs During COVID-19","year":2020,"lang":"en","type":"letter","venue":"Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques","topic":"Diversity and Career in Medicine","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Content (measure theory); Matching (statistics); Computer science; 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Medicine; Mathematics; Virology; Pathology; Infectious disease (medical specialty)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001594938,0.0001636575,0.0003742957,0.0005213989,0.006377933,0.001468965,0.0007593217,0.00626875,0.01102883],"category_scores_gemma":[0.01289593,0.0002474855,0.0002692655,0.0006700653,0.0009852992,0.001801272,0.001807515,0.006256037,0.001628736],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005435959,"about_ca_system_score_gemma":0.006749569,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05785257,"about_ca_topic_score_gemma":0.1170366,"domain_scores_codex":[0.997521,0.0007593129,0.0001543977,0.0001778638,0.0003123625,0.001075121],"domain_scores_gemma":[0.9962564,0.000612157,0.0003192471,0.00006432614,0.0003768971,0.002370982],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003137038,0.001117981,0.1359213,0.0002052201,0.00003482727,0.06632262,0.006977737,0.0001976122,0.001354321,0.005376116,0.6843011,0.09787744],"study_design_scores_gemma":[0.0004313404,0.001440465,0.3830044,0.00522452,0.0001219628,0.125013,0.1226438,0.003677685,0.001300423,0.02472826,0.3321569,0.0002573679],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.1047496,0.001126895,0.0002805441,0.8597683,0.004724693,0.0001090167,0.0001063713,0.0000303959,0.02910416],"genre_scores_gemma":[0.4875795,0.002378697,0.0008490785,0.4827647,0.005201996,0.0001988581,0.0001385649,0.00007140837,0.02081713],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.05785257,"threshold_uncertainty_score":0.1150317,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07130629644122695,"score_gpt":0.3053833737729544,"score_spread":0.2340770773317274,"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."}}