{"id":"W4396812245","doi":"10.1016/j.cct.2024.107560","title":"Designing a Bayesian adaptive clinical trial to evaluate novel mechanical ventilation strategies in acute respiratory failure using integrated nested Laplace approximations","year":2024,"lang":"en","type":"article","venue":"Contemporary Clinical Trials","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"SickKids Foundation; University of Toronto; University Health Network; Institute for Clinical Evaluative Sciences; Public Health Ontario; Hospital for Sick Children","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Medicine; Bayesian probability; Mechanical ventilation; Acute respiratory failure; Clinical trial; Intensive care medicine; Respiratory failure; Randomized controlled trial; Laplace transform; Artificial intelligence; Anesthesia; Surgery; Internal medicine","routes":{"ca_aff":true,"ca_fund":true,"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":[0.07910278,0.001713636,0.004367084,0.001074884,0.0004415119,0.002346679,0.002481158,0.003651619,0.003891943],"category_scores_gemma":[0.1820715,0.001548881,0.002409982,0.0007511055,0.003002671,0.003084727,0.002415731,0.004933924,0.0004832338],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00135612,"about_ca_system_score_gemma":0.004968378,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009294974,"about_ca_topic_score_gemma":0.000780633,"domain_scores_codex":[0.9374343,0.05688658,0.00123984,0.002279436,0.001529902,0.0006299556],"domain_scores_gemma":[0.8601754,0.1287329,0.005272095,0.002536979,0.001790051,0.00149249],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.06643659,0.004468374,0.01171459,0.002875959,0.006372022,0.0004428036,0.000647869,0.5508269,0.004293708,0.1198014,0.00359567,0.228524],"study_design_scores_gemma":[0.02615199,0.01158858,0.001441817,0.0004110076,0.002062483,0.0001227363,0.00007108834,0.8428501,0.001523487,0.1110519,0.00262847,0.00009630716],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07518145,0.001841599,0.9093952,0.003504026,0.0003368446,0.007081224,0.00024498,0.0003801141,0.002034525],"genre_scores_gemma":[0.6140259,0.0007166332,0.3692062,0.001798455,0.0002074273,0.01257164,0.0002550205,0.00006151417,0.001157171],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.07910278,"threshold_uncertainty_score":0.4183405,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.8742907154392562,"score_gpt":0.6583211563281324,"score_spread":0.2159695591111238,"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."}}