{"id":"W4286296437","doi":"10.1200/jco.2022.40.16_suppl.1573","title":"Interface software can markedly reduce time and improve accuracy for clinical trial data transfer from EMR to EDC: The results of two measure of work time studies comparing commercially available clinical data transfer software to current practice manual data transfer.","year":2022,"lang":"en","type":"article","venue":"Journal of Clinical Oncology","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vancouver Coastal Health","funders":"","keywords":"Medicine; Data entry; Electronic data capture; Clinical trial; Transfer (computing); Demographics; Medical record; Medical physics; Surgery; Computer science; Database; Internal medicine","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"],"consensus_categories":[],"category_scores_codex":[0.03482782,0.000803087,0.0006805754,0.001082627,0.0003042491,0.00162528,0.001240124,0.001463025,0.003609112],"category_scores_gemma":[0.09667981,0.0004054646,0.001637433,0.001136192,0.0009063264,0.002085952,0.001700185,0.001185656,0.0007510669],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006210319,"about_ca_system_score_gemma":0.0007796919,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005159631,"about_ca_topic_score_gemma":0.0008088829,"domain_scores_codex":[0.9411504,0.04826758,0.002939458,0.002212093,0.004867434,0.0005630715],"domain_scores_gemma":[0.8631799,0.1040054,0.0140695,0.01008004,0.006719176,0.001945839],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.3124259,0.02768739,0.08778698,0.003360055,0.003651597,0.0002018182,0.002858716,0.001928611,0.02511683,0.0006842152,0.005402403,0.5288954],"study_design_scores_gemma":[0.02847498,0.5541577,0.366859,0.0004495241,0.003039686,0.0004697563,0.0008647448,0.007074615,0.02462696,0.0007713798,0.01303156,0.0001800687],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9768535,0.003083727,0.01230092,0.0003971498,0.0001861913,0.002607643,0.0004830956,0.0002552737,0.003832384],"genre_scores_gemma":[0.9646834,0.0006420433,0.02959135,0.0004854763,0.0001651639,0.002329231,0.000574034,0.0001013608,0.001428091],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9651722,"threshold_uncertainty_score":0.1841894,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6863036870456535,"score_gpt":0.6561498057262362,"score_spread":0.03015388131941732,"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."}}