{"id":"W3197781780","doi":"10.1186/s13063-021-05571-4","title":"Correction to: The role of machine learning in clinical research: transforming the future of evidence generation","year":2021,"lang":"en","type":"article","venue":"Trials","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; Vector Institute; Boehringer Ingelheim (Canada); University of Alberta","funders":"","keywords":"Medicine; Alternative medicine; Data science; MEDLINE; Engineering ethics; Computer science; Pathology; Engineering; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01568035,0.003467035,0.006488291,0.007143865,0.004983185,0.01121181,0.008934459,0.02216204,0.09691368],"category_scores_gemma":[0.3105237,0.002660997,0.003399929,0.006701397,0.006512119,0.004374844,0.003621343,0.02877631,0.053204],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.009661626,"about_ca_system_score_gemma":0.01303644,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01365587,"about_ca_topic_score_gemma":0.01607985,"domain_scores_codex":[0.9722564,0.006875288,0.007120557,0.003432954,0.008044928,0.002269883],"domain_scores_gemma":[0.7398698,0.09319472,0.01488551,0.01654361,0.1240012,0.0115052],"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":[0.00006908138,0.000007770603,0.00007726447,0.0004234925,0.00004472151,0.0002075722,0.00003763476,0.00005261357,0.00003421594,0.0007897581,0.9947749,0.003480939],"study_design_scores_gemma":[0.0004308299,0.00005517327,0.001640112,0.00228143,0.0001835317,0.001145419,0.0002346972,0.0009655827,0.0004618561,0.005835342,0.9865988,0.000167044],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"editorial","genre_scores_codex":[0.00006093966,0.0006851794,0.0004790696,0.07568025,0.9210458,0.00005097885,0.0009020456,0.0002692772,0.000826394],"genre_scores_gemma":[0.007664873,0.004882304,0.004250489,0.1641068,0.7674552,0.0006043969,0.001434874,0.00102749,0.04857365],"genre_candidate":"editorial","genre_consensus":"editorial","teacher_disagreement_score":0.09691368,"threshold_uncertainty_score":0.3242086,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4794492439805076,"score_gpt":0.5284113496710181,"score_spread":0.04896210569051052,"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."}}