{"id":"W6902080693","doi":"10.6084/m9.figshare.16909630","title":"Additional file 2 of Assessing the suitability of general practice electronic health records for clinical prediction model development: a data quality assessment","year":2021,"lang":"en","type":"article","venue":"Figshare","topic":"Data Quality and Management","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Data quality; Record linkage; Health records; General practice; Quality (philosophy); Health data; Data collection; Quality assessment","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00945932,0.0008736764,0.0009665074,0.003914575,0.001054667,0.002020817,0.001956811,0.001219084,0.8333657],"category_scores_gemma":[0.1457751,0.0005686496,0.001156445,0.00552683,0.0003332454,0.001933379,0.001516643,0.0009920374,0.08028209],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001908849,"about_ca_system_score_gemma":0.003943256,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00834058,"about_ca_topic_score_gemma":0.01080128,"domain_scores_codex":[0.9951856,0.001493166,0.001347271,0.0006069715,0.001092537,0.0002745424],"domain_scores_gemma":[0.8188555,0.1496352,0.007976958,0.006861991,0.01547879,0.001191716],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0004945148,0.0001316982,0.003528955,0.003075822,0.00009918449,0.00005163694,0.0001293239,0.0005332869,0.00006791563,0.001229696,0.9768592,0.01379872],"study_design_scores_gemma":[0.0130978,0.0006984775,0.06930359,0.009208524,0.0007238686,0.000651638,0.001366905,0.006871786,0.001828295,0.01861374,0.8773255,0.0003098767],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"other","genre_scores_codex":[0.0004850706,0.00001176666,0.0007766552,0.0002111236,0.0000268061,0.0005884139,0.9960895,0.0002644939,0.001546137],"genre_scores_gemma":[0.0246786,0.0001425721,0.01892301,0.001004651,0.0001974119,0.01918609,0.9186962,0.001309042,0.01586253],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.8333657,"threshold_uncertainty_score":0.2376835,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6515578119347073,"score_gpt":0.5949122324697573,"score_spread":0.05664557946494997,"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."}}