{"id":"W4415940927","doi":"10.1016/j.annonc.2025.08.3749","title":"3207eP Accelerating eSource data collection for accurate clinical research","year":2025,"lang":"en","type":"article","venue":"Annals of Oncology","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"AstraZeneca (Canada)","funders":"","keywords":"Electronic data capture; Data collection; Electronic medical record; Data quality; Clinical trial; Quality (philosophy); Medical record; Electronic data","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":[0.03053087,0.001297044,0.0009177606,0.008698707,0.001470556,0.00719529,0.002938336,0.001974338,0.280349],"category_scores_gemma":[0.1121148,0.001013048,0.001520856,0.005313332,0.0008002399,0.006411824,0.009890211,0.00243368,0.2221437],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001997849,"about_ca_system_score_gemma":0.01278797,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008566535,"about_ca_topic_score_gemma":0.00720957,"domain_scores_codex":[0.9779612,0.006775627,0.001950188,0.001677691,0.01054667,0.001088516],"domain_scores_gemma":[0.8439761,0.03541971,0.00569539,0.03510045,0.0743364,0.005471901],"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":[0.0001787903,0.0002383467,0.005229305,0.0003532739,0.00005148487,0.00007914832,0.0001711906,0.0001562032,0.001440427,0.005792432,0.7576063,0.2287031],"study_design_scores_gemma":[0.0001593714,0.0000651657,0.008583198,0.0004042239,0.00004550701,0.0001240619,0.0001949956,0.001290507,0.003518224,0.005122046,0.9804519,0.00004091374],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"methods","genre_scores_codex":[0.01124229,0.00119292,0.1938074,0.03999471,0.007693382,0.006632356,0.211175,0.0758438,0.4524181],"genre_scores_gemma":[0.0388132,0.002223608,0.384386,0.02159214,0.00441622,0.00564201,0.2755218,0.01411857,0.2532864],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9694691,"threshold_uncertainty_score":0.9378607,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.8986521079377753,"score_gpt":0.7701624445116991,"score_spread":0.1284896634260762,"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."}}