{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002964297,0.0002386759,0.0007546722,0.0001121338,0.000798875,0.00000969649,0.0004619807,0.00008032254,0.004401283],"category_scores_gemma":[0.03502342,0.0001393055,0.0002362496,0.0007207951,0.0005685493,0.00004605624,0.0002626718,0.001497749,0.00007442673],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001644057,"about_ca_system_score_gemma":0.0004148055,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003199697,"about_ca_topic_score_gemma":0.0001513318,"domain_scores_codex":[0.9956257,0.0006071117,0.0006729378,0.0006192569,0.001975166,0.000499877],"domain_scores_gemma":[0.9718204,0.02674875,0.000112968,0.0008276813,0.0001371141,0.0003531524],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.04694068,0.002937092,0.01433639,0.0004234982,0.001412716,0.001220299,0.03185479,0.0002842163,0.07422528,0.002480948,0.5055721,0.318312],"study_design_scores_gemma":[0.09717734,0.005304188,0.01478066,0.000161052,0.0004902969,0.000004838296,0.004784305,0.0004166029,0.0001803355,0.002586878,0.8740388,0.00007465882],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5158522,0.001608663,0.001281435,0.4723961,0.001337326,0.002228156,0.00009389231,0.0002149673,0.004987286],"genre_scores_gemma":[0.9434813,0.01118211,0.00008379607,0.02984361,0.002982384,0.0004550572,0.0001389413,0.00006364674,0.01176912],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4425525,"threshold_uncertainty_score":0.9965088,"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."}}