{"id":"W2906793936","doi":"10.12688/f1000research.15869.1","title":"Identifying stroke therapeutics from preclinical models: A protocol for a novel application of network meta-analysis","year":2019,"lang":"en","type":"preprint","venue":"F1000Research","topic":"Meta-analysis and systematic reviews","field":"Decision Sciences","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary; Ottawa Hospital; Heart and Stroke Foundation; University of Ottawa","funders":"Ottawa Hospital; Ottawa Hospital Anesthesia Alternate Funds Association; National Centre for the Replacement, Refinement and Reduction of Animals in Research; University of Bristol; Canadian Institutes of Health Research; National Institute for Health and Care Research; University Hospitals Bristol NHS Foundation Trust; Medical Research Council; University of Ottawa","keywords":"Medicine; Stroke (engine); Meta-analysis; Intensive care medicine; Neuroprotection; Clinical trial; Bioinformatics; Pathology; Internal medicine; Biology","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","metaepi_broad","scholarly_communication","open_science","insufficient_payload"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.1971675,0.0006635536,0.01379498,0.001105788,0.0001614479,0.001689326,0.0077521,0.0006601521,0.005632111],"category_scores_gemma":[0.008974976,0.0003416023,0.02122613,0.00269466,0.0001539752,0.0002152008,0.002416668,0.001028643,0.0005368061],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007530567,"about_ca_system_score_gemma":0.0005213949,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002786221,"about_ca_topic_score_gemma":0.0001986548,"domain_scores_codex":[0.9546205,0.0118126,0.01641323,0.003121604,0.01338141,0.0006506403],"domain_scores_gemma":[0.9428282,0.02621673,0.01167653,0.01312184,0.005919695,0.0002369933],"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":[0.0003176269,0.0007051227,0.00744438,0.00122539,0.4715887,6.199932e-7,0.0006154431,0.4861424,0.0002240294,0.005151206,0.01736904,0.009216097],"study_design_scores_gemma":[0.0003516149,0.00004372311,0.0005603904,0.00002187251,0.09962811,1.513435e-7,0.00008525852,0.7887115,0.00003092383,0.08199789,0.02831039,0.0002581858],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"protocol","genre_scores_codex":[0.0001203788,0.0007522597,0.7790087,0.000405759,0.00007995273,0.2173197,0.001177706,0.000009684942,0.001125834],"genre_scores_gemma":[0.03951022,0.000017618,0.1710775,0.0001709327,0.0004404546,0.771412,0.0003450493,0.00008797118,0.01693821],"genre_candidate":"protocol","genre_consensus":null,"teacher_disagreement_score":0.6079313,"threshold_uncertainty_score":0.9999036,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.9764130104948826,"score_gpt":0.7014080301553236,"score_spread":0.275004980339559,"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."}}