{"id":"W3200504465","doi":"10.2196/13790","title":"Applicability of Different Electronic Record Types for Use in Patient Recruitment Support Systems: Comparative Analysis","year":2021,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Medical record; Electronic medical record; Software deployment; Electronic health record; Information system; Patient recruitment; Clinical decision support system; Medicine; Knowledge management; Medical emergency; Decision support system; Clinical trial; Computer science; Health care; Engineering; Data mining","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.1215124,0.0006329849,0.0009633559,0.006072262,0.001022488,0.004353419,0.001565527,0.001695261,0.004458529],"category_scores_gemma":[0.3929627,0.0005589408,0.003872695,0.004457402,0.001686638,0.007087665,0.003533522,0.00143301,0.0009710644],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003160238,"about_ca_system_score_gemma":0.003541408,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009986974,"about_ca_topic_score_gemma":0.001155095,"domain_scores_codex":[0.8332077,0.1004921,0.03252272,0.004495384,0.02686467,0.002417357],"domain_scores_gemma":[0.3823711,0.5147893,0.04375589,0.01520274,0.04150591,0.002374938],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.02306475,0.004009713,0.4618705,0.02086195,0.003000591,0.0005489939,0.03266133,0.001597836,0.003547357,0.004787515,0.001841114,0.4422083],"study_design_scores_gemma":[0.002589059,0.03632169,0.8527446,0.01539505,0.005756343,0.001676011,0.04349257,0.008268201,0.01168268,0.00343886,0.01822398,0.0004108559],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9674969,0.00575656,0.0111808,0.0007272672,0.0001257226,0.006327493,0.001188968,0.0001314239,0.00706492],"genre_scores_gemma":[0.97578,0.00206968,0.01491414,0.0002938762,0.00006590843,0.005468648,0.0009262445,0.00005955671,0.0004219302],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1215124,"threshold_uncertainty_score":0.6426269,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3265623490826519,"score_gpt":0.5633665508028513,"score_spread":0.2368042017201994,"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."}}