{"id":"W4281649826","doi":"10.1111/tme.12882","title":"Lessons learned from the <scp>CONCOR</scp>‐1 trial","year":2022,"lang":"en","type":"article","venue":"Transfusion Medicine","topic":"COVID-19 Clinical Research Studies","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Blood Services; Health Sciences Centre; Sunnybrook Health Science Centre; Université de Montréal; McMaster University; Queen's University; Centre Hospitalier Universitaire Sainte-Justine; Kingston Health Sciences Centre; McMaster University Medical Centre","funders":"","keywords":"Clinical trial; Pandemic; Coronavirus disease 2019 (COVID-19); Workload; Economic shortage; Medicine; 2019-20 coronavirus outbreak; CLARITY; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Medical education; Business; Public relations; Family medicine; Operations research; Political science; Computer science; Engineering; Virology; Internal medicine; Disease; Infectious disease (medical specialty)","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":[],"consensus_categories":[],"category_scores_codex":[0.1577037,0.001296029,0.002379806,0.001125256,0.001786612,0.009721128,0.003469621,0.008337793,0.007562128],"category_scores_gemma":[0.260333,0.0006853081,0.003352163,0.001579392,0.003547867,0.01164788,0.003106829,0.02070529,0.002828797],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003756306,"about_ca_system_score_gemma":0.01688358,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004759745,"about_ca_topic_score_gemma":0.0125121,"domain_scores_codex":[0.891502,0.08758945,0.008773631,0.002430974,0.007270784,0.002433166],"domain_scores_gemma":[0.6662412,0.27443,0.00777618,0.01241741,0.02556222,0.01357296],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.002921162,0.0003812824,0.003786981,0.01019591,0.001167667,0.001507753,0.002868979,0.001952725,0.0005223584,0.02874164,0.5793344,0.3666191],"study_design_scores_gemma":[0.004444802,0.003028625,0.004539039,0.02851404,0.00185194,0.001772299,0.003335282,0.002215659,0.001453012,0.1170633,0.8314055,0.0003765197],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.003838981,0.0542885,0.007944474,0.9047183,0.01838898,0.0007073536,0.001146641,0.0002837667,0.008682971],"genre_scores_gemma":[0.08087971,0.09548571,0.06958534,0.6805666,0.05832063,0.003488986,0.002074259,0.0008775187,0.008721313],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1577037,"threshold_uncertainty_score":0.8340271,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.180634904250739,"score_gpt":0.4574941193669987,"score_spread":0.2768592151162597,"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."}}