{"id":"W1973080775","doi":"10.1503/cmaj.1080110","title":"Number needed to treat and baseline risks","year":2008,"lang":"en","type":"letter","venue":"Canadian Medical Association Journal","topic":"Health Systems, Economic Evaluations, Quality of Life","field":"Economics, Econometrics and Finance","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Blood Services","funders":"","keywords":"Baseline (sea); Computer science; Data science; Medicine; Risk analysis (engineering)","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.03366864,0.0007674251,0.00382011,0.002265418,0.001303534,0.003511718,0.003727803,0.0237025,0.01017423],"category_scores_gemma":[0.2887768,0.0005557219,0.001985438,0.002309249,0.004524175,0.007750002,0.001654164,0.04168762,0.005650451],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007808449,"about_ca_system_score_gemma":0.003569714,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00148159,"about_ca_topic_score_gemma":0.001386198,"domain_scores_codex":[0.9403348,0.03861,0.005022647,0.002442358,0.01239471,0.001195564],"domain_scores_gemma":[0.7957064,0.1741095,0.00971759,0.003633794,0.01389361,0.002939131],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0008325181,0.00006592055,0.001074485,0.001394681,0.0002019409,0.0003384309,0.0002085662,0.0008374719,0.0001204995,0.06421965,0.867846,0.06285988],"study_design_scores_gemma":[0.001049779,0.0006180728,0.003054988,0.007288722,0.00030105,0.004361488,0.0003530054,0.005172405,0.0005593661,0.2825062,0.6944407,0.0002941993],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.0003649141,0.01602894,0.003006678,0.9562159,0.01523959,0.00008413326,0.0002670062,0.0000404345,0.008752431],"genre_scores_gemma":[0.02201586,0.01231116,0.006790336,0.8950748,0.05895244,0.0008739047,0.0003215095,0.0001151282,0.003544874],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.9663314,"threshold_uncertainty_score":0.178059,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2136110246988124,"score_gpt":0.3900125631308411,"score_spread":0.1764015384320287,"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."}}