{"id":"W2788158671","doi":"10.3399/bjgp18x695237","title":"Finding and using routine clinical datasets for observational research and quality improvement","year":2018,"lang":"en","type":"article","venue":"British Journal of General Practice","topic":"Primary Care and Health Outcomes","field":"Health Professions","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute for Clinical Evaluative Sciences; North York General Hospital; Public Health Ontario; University of Toronto","funders":"Medical Research Council; National Institute for Health and Care Research; Cancer Research UK","keywords":"Observational study; Primary care; Record linkage; Medicine; Resource (disambiguation); Medical record; Health records; Quality (philosophy); Population health; Variety (cybernetics); Health care; Data science; Diversity (politics); Family medicine; Population; Computer science; Environmental health","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","sts"],"consensus_categories":[],"category_scores_codex":[0.02034253,0.00007670376,0.0003211572,0.00007324427,0.001445462,0.00006932308,0.0001290503,0.0001379328,0.00006427969],"category_scores_gemma":[0.009850165,0.00007917528,0.00004040697,0.0001087583,0.0001703697,0.0008586907,0.000221003,0.0009903774,0.000002854782],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001991173,"about_ca_system_score_gemma":0.001255254,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001555498,"about_ca_topic_score_gemma":0.000379706,"domain_scores_codex":[0.9962505,0.001424816,0.001285621,0.0002075125,0.0004257314,0.0004058705],"domain_scores_gemma":[0.9915406,0.005530818,0.0008815699,0.0001239829,0.001682661,0.0002403324],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.003607514,0.0005181599,0.1942352,0.0009175152,0.0003008691,0.0001428818,0.0007927799,0.000001114196,0.003607666,0.006180748,0.5953648,0.1943307],"study_design_scores_gemma":[0.005224577,0.001820402,0.360629,0.0003701307,0.0001060337,0.0006380356,0.001613765,0.0002307961,0.00005029144,0.002325674,0.6267749,0.0002164151],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9811208,0.001494969,0.001275582,0.01374146,0.001051025,0.0004935029,0.0002827014,0.000006187942,0.0005338115],"genre_scores_gemma":[0.5863556,0.01009376,0.2879556,0.09040871,0.02219126,0.00004277139,0.0002584679,0.00008878949,0.002605053],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3947652,"threshold_uncertainty_score":0.9998545,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6768788137521777,"score_gpt":0.6695427752188503,"score_spread":0.007336038533327427,"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."}}