{"id":"W4205246393","doi":"10.2196/preprints.26718","title":"Clinical Trial Data Sharing for COVID-19–Related Research (Preprint)","year":2020,"lang":"en","type":"preprint","venue":"","topic":"COVID-19 and healthcare impacts","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; University of British Columbia; Impact","funders":"","keywords":"Sample size determination; Context (archaeology); Statistical power; Clinical trial; Coronavirus disease 2019 (COVID-19); Pandemic; Sample (material); Data sharing; Perspective (graphical); Psychology; Medicine; Computer science; Statistics; Alternative medicine; Geography; Mathematics; Artificial intelligence","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","open_science"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.1959638,0.001143874,0.003235938,0.00783069,0.002020386,0.01938001,0.003773475,0.007984515,0.2502711],"category_scores_gemma":[0.5729152,0.002564307,0.005155373,0.01339498,0.003746969,0.007848326,0.01064316,0.00682252,0.135696],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006612129,"about_ca_system_score_gemma":0.02122302,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001432647,"about_ca_topic_score_gemma":0.001316093,"domain_scores_codex":[0.7603927,0.166244,0.03992515,0.007769248,0.02265237,0.00301653],"domain_scores_gemma":[0.2932673,0.5035291,0.05091801,0.09549545,0.04514246,0.01164761],"domain_codex":null,"domain_gemma":"reproducibility","domain_candidate":"reproducibility","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0007308695,0.00004268396,0.0006144056,0.005504117,0.0004656354,0.0001116321,0.0004698667,0.0007748377,0.0004140828,0.02195873,0.90218,0.06673323],"study_design_scores_gemma":[0.0007774204,0.000149741,0.002089614,0.005386174,0.0001124541,0.0002048655,0.0001908802,0.00150356,0.0004723669,0.04569917,0.94328,0.0001336805],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.003127906,0.009875013,0.204786,0.2948293,0.08136009,0.02072718,0.191926,0.03892064,0.1544479],"genre_scores_gemma":[0.06572683,0.01862747,0.2950535,0.1381994,0.07899812,0.06480908,0.2158764,0.03454521,0.08816409],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.9962265,"threshold_uncertainty_score":0.9915197,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.8894561206790608,"score_gpt":0.6985040956323589,"score_spread":0.1909520250467018,"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."}}