{"id":"W4252803570","doi":"10.2196/preprints.23789","title":"A Fully Collaborative, Noteless Electronic Medical Record Designed to Minimize Information Chaos: Software Design and Feasibility Study (Preprint)","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Health Sciences Centre; University of Toronto; Sunnybrook Health Science Centre","funders":"","keywords":"Documentation; Computer science; Preprint; Workflow; Information system; Medical record; Incentive; Knowledge management; Data science; World Wide Web; Medicine; Engineering; Database","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":[0.01838342,0.0006804038,0.0004840518,0.0009417359,0.0007187185,0.001623183,0.001955148,0.001480275,0.005226622],"category_scores_gemma":[0.04957849,0.0005622352,0.0007421132,0.0004924503,0.001106085,0.001866541,0.00189916,0.0007628847,0.0009915314],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001134488,"about_ca_system_score_gemma":0.003442453,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001242988,"about_ca_topic_score_gemma":0.001070264,"domain_scores_codex":[0.9902,0.007111969,0.0008235862,0.0005836498,0.0008606579,0.000420165],"domain_scores_gemma":[0.9480177,0.03871161,0.001883352,0.00410514,0.004835569,0.002446541],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.01972114,0.06516597,0.05561027,0.006201123,0.000526714,0.003187961,0.01823288,0.05098287,0.05018327,0.01194292,0.01111394,0.707131],"study_design_scores_gemma":[0.03836603,0.3883645,0.0950191,0.00107742,0.001692664,0.002023173,0.01421377,0.3389302,0.06756435,0.00822657,0.04361603,0.0009061623],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.9061509,0.00007512698,0.07608443,0.0004067904,0.0001030627,0.01338454,0.0003969501,0.001055742,0.002342455],"genre_scores_gemma":[0.6714313,0.0001104629,0.3086425,0.0002925826,0.00005279903,0.01678482,0.0004883417,0.000180569,0.002016569],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.01838342,"threshold_uncertainty_score":0.09722197,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08330495034644285,"score_gpt":0.4257885420695414,"score_spread":0.3424835917230986,"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."}}