{"id":"W4376130123","doi":"10.1016/j.cmi.2023.05.003","title":"Unlocking the DOOR—how to design, apply, analyse, and interpret desirability of outcome ranking endpoints in infectious diseases clinical trials","year":2023,"lang":"en","type":"review","venue":"Clinical Microbiology and Infection","topic":"Meta-analysis and systematic reviews","field":"Decision Sciences","cited_by":41,"is_retracted":false,"has_abstract":false,"ca_institutions":"Health Sciences Centre; University of Toronto; Sunnybrook Health Science Centre","funders":"","keywords":"Ranking (information retrieval); Clinical trial; Medicine; MEDLINE; Randomized controlled trial; Metric (unit); Outcome (game theory); Clinical endpoint; Clinical study design; Intensive care medicine; Post hoc; Computer science; Artificial intelligence; Surgery; Internal medicine; Business; Marketing; Political science; Economics","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":["metaresearch"],"category_scores_codex":[0.6973999,0.006997835,0.03924086,0.01298936,0.002967172,0.02233692,0.01027536,0.01097615,0.008096667],"category_scores_gemma":[0.8783835,0.007769257,0.03506444,0.0102248,0.01150477,0.02491474,0.01221688,0.02303938,0.001926897],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00543449,"about_ca_system_score_gemma":0.01802488,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003661838,"about_ca_topic_score_gemma":0.005988167,"domain_scores_codex":[0.1478221,0.7461362,0.07197704,0.0105032,0.02272308,0.0008383458],"domain_scores_gemma":[0.04169619,0.9039587,0.01841026,0.02192837,0.01302453,0.0009818691],"domain_codex":"methods","domain_gemma":"methods","domain_candidate":"methods","domain_consensus":"methods","study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.009004277,0.0004087952,0.007859732,0.1880473,0.1848713,0.0002731786,0.003347147,0.008738285,0.000833084,0.02930627,0.0311055,0.5362053],"study_design_scores_gemma":[0.01963116,0.00421393,0.01095639,0.1693095,0.1953411,0.0006786279,0.001633253,0.06495987,0.003214985,0.44336,0.08450302,0.002198212],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00928939,0.3248973,0.5631604,0.05064274,0.01227802,0.0279747,0.005035577,0.002807262,0.003914653],"genre_scores_gemma":[0.1113688,0.03772549,0.8040283,0.0103079,0.003897409,0.03017773,0.0009495344,0.0009272607,0.0006175212],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3026001,"threshold_uncertainty_score":0.3731598,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.9033804032143201,"score_gpt":0.6662354969344657,"score_spread":0.2371449062798544,"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."}}