{"id":"W2889669580","doi":"10.23889/ijpds.v3i4.996","title":"Linking medical licensing examination scores with longitudinal physician practice data using a privacy preserving protocol","year":2018,"lang":"en","type":"article","venue":"International Journal for Population Data Science","topic":"Medical Malpractice and Liability Issues","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Medical Council of Canada; College of Physicians and Surgeons of Ontario","funders":"","keywords":"Licensure; Cohort; Encryption; Protocol (science); Competence (human resources); Credential; Medical record; Information privacy; Privacy law; Key (lock); Internet privacy; Computer security; Computer science; Business; Medicine; Medical education; Psychology; Privacy policy","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.1713264,0.0008537727,0.001030158,0.003826122,0.001881081,0.004830783,0.003560962,0.002699684,0.01227743],"category_scores_gemma":[0.3625745,0.001057665,0.002262108,0.007375252,0.003777193,0.005391871,0.005889427,0.002933481,0.003296136],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004835349,"about_ca_system_score_gemma":0.01432825,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008626209,"about_ca_topic_score_gemma":0.00491372,"domain_scores_codex":[0.7763392,0.1515207,0.03072045,0.01529103,0.02296354,0.003164918],"domain_scores_gemma":[0.5466841,0.2241582,0.06663841,0.1204911,0.0395335,0.002494651],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.01065111,0.002426164,0.2867103,0.003481605,0.001580046,0.001125774,0.01627886,0.02904966,0.004391415,0.1719758,0.05285367,0.4194756],"study_design_scores_gemma":[0.003707855,0.006561993,0.2470724,0.005012816,0.001323431,0.001310747,0.007626871,0.1452672,0.04220904,0.1776772,0.3613769,0.0008535723],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1857965,0.0006159857,0.6146022,0.009824724,0.0007624485,0.06773061,0.09212447,0.002592881,0.02595013],"genre_scores_gemma":[0.5315868,0.0004304647,0.2778838,0.001748461,0.0002816248,0.1455511,0.03180562,0.0002425448,0.01046961],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1713264,"threshold_uncertainty_score":0.9060714,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3491210849388082,"score_gpt":0.5967497560950241,"score_spread":0.2476286711562159,"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."}}