{"id":"W2996712851","doi":"10.1016/j.jval.2019.09.2194","title":"PNS294 REVIEW OF ANALYTICAL METHODS FOR ANALYSIS OF PATIENT REPORTED OUTCOME (PRO) DATA IN THE PRESENCE OF CENSORING DUE TO DEATH","year":2019,"lang":"en","type":"article","venue":"Value in Health","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Eli Lilly (Canada)","funders":"","keywords":"Censoring (clinical trials); Interpretability; Computer science; Statistics; Continuation; Econometrics; Data mining; Mathematics; Machine learning","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.09765188,0.001941095,0.003187864,0.009586322,0.0008065231,0.004040543,0.003702682,0.001958167,0.008898905],"category_scores_gemma":[0.1997601,0.001319564,0.004088741,0.01048545,0.002321773,0.001852126,0.00231613,0.003669217,0.005541896],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002471932,"about_ca_system_score_gemma":0.009302776,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006263423,"about_ca_topic_score_gemma":0.006922917,"domain_scores_codex":[0.9142753,0.05843622,0.01007854,0.004091351,0.0126892,0.0004293984],"domain_scores_gemma":[0.7666688,0.1877067,0.009435484,0.009360267,0.02608047,0.000748151],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"systematic_review","study_design_scores_codex":[0.0003765599,0.0001366222,0.00925924,0.02043223,0.002172352,0.0002331137,0.0002805577,0.00142333,0.0007701154,0.01729319,0.06120615,0.8864165],"study_design_scores_gemma":[0.000244942,0.001005236,0.05663512,0.05177852,0.003299631,0.003617423,0.0003907896,0.01133997,0.005393752,0.04646923,0.8194318,0.0003934349],"study_design_candidate":"systematic_review","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.002399854,0.5490199,0.4101269,0.01177289,0.004035083,0.001478066,0.00919106,0.0009124328,0.0110638],"genre_scores_gemma":[0.01835465,0.5821245,0.3656058,0.007232578,0.005269961,0.003833759,0.009421978,0.001061948,0.007094778],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9023481,"threshold_uncertainty_score":0.5164387,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6611683677528487,"score_gpt":0.6394010307986849,"score_spread":0.02176733695416377,"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."}}