{"id":"W4319008673","doi":"10.1161/str.54.suppl_1.tp30","title":"Abstract TP30: Enrolling Patients During Covid-19: Lessons From The Timeless Clinical Trial","year":2023,"lang":"en","type":"article","venue":"Stroke","topic":"COVID-19 and healthcare impacts","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Medicine; Patient recruitment; Clinical trial; Informed consent; Stroke (engine); Coronavirus disease 2019 (COVID-19); Pandemic; Telemedicine; Institutional review board; Medical emergency; Randomization; Workflow; Family medicine; Emergency medicine; Internal medicine; Alternative medicine; Disease; Health care; Surgery; Pathology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.1302778,0.0009030213,0.001439469,0.0008981742,0.004871956,0.01147008,0.005523025,0.007276346,0.01679305],"category_scores_gemma":[0.2984532,0.0005916097,0.001949565,0.001551516,0.004870696,0.01055334,0.007104881,0.01796392,0.00791672],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005626919,"about_ca_system_score_gemma":0.02825324,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006203832,"about_ca_topic_score_gemma":0.01281073,"domain_scores_codex":[0.9127401,0.07173771,0.003232577,0.002177651,0.007166323,0.002945606],"domain_scores_gemma":[0.6875438,0.207844,0.01105836,0.01986543,0.02918176,0.04450663],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001328618,0.0006330814,0.005475441,0.00150976,0.0001364543,0.0009208487,0.00565243,0.001005307,0.0003279263,0.0105086,0.6143871,0.3581145],"study_design_scores_gemma":[0.003989605,0.004894339,0.01701258,0.01119829,0.0004429223,0.00223145,0.01503235,0.005432862,0.001984914,0.1301922,0.807048,0.0005406456],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.01660931,0.01282572,0.02167727,0.9035622,0.01168349,0.001396297,0.001123259,0.001473118,0.0296494],"genre_scores_gemma":[0.2682607,0.03237879,0.104289,0.5282853,0.03015275,0.00667733,0.00301674,0.00340688,0.0235324],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.8697222,"threshold_uncertainty_score":0.6889831,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2211077546306437,"score_gpt":0.4927318655507231,"score_spread":0.2716241109200794,"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."}}