{"id":"W2890579496","doi":"10.23889/ijpds.v3i4.951","title":"Inferring sensitivity and specificity of phenotyping algorithms using positive and negative predictive value in validation study in observational health data","year":2018,"lang":"en","type":"article","venue":"International Journal for Population Data Science","topic":"Health, Environment, Cognitive Aging","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Observational study; Sensitivity (control systems); Computer science; Data mining; Algorithm; Machine learning; Sample size determination; Population; Statistics; Medicine; Mathematics; Engineering","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":[0.06733172,0.00119596,0.001217416,0.003345986,0.0008547015,0.002945304,0.002242468,0.002407531,0.001358446],"category_scores_gemma":[0.2047772,0.0006906472,0.001951755,0.001668569,0.0023933,0.00203238,0.002363391,0.001678839,0.0002436671],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001137813,"about_ca_system_score_gemma":0.001776482,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003859208,"about_ca_topic_score_gemma":0.003393328,"domain_scores_codex":[0.952984,0.03606686,0.00245705,0.00450496,0.003147613,0.0008395023],"domain_scores_gemma":[0.701872,0.2730393,0.008831056,0.01000727,0.005391082,0.0008592074],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001096272,0.0002895075,0.7286043,0.0007355898,0.002003409,0.0004894405,0.0006190773,0.1290145,0.002577137,0.01097124,0.002105097,0.1214943],"study_design_scores_gemma":[0.0002856995,0.0005342702,0.1200513,0.0004374638,0.0009621938,0.0007907544,0.000366466,0.8191106,0.01214401,0.04174157,0.003398178,0.0001774718],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.4280004,0.001790522,0.5641915,0.001074032,0.0001571987,0.0004407487,0.001423011,0.0004048084,0.002517831],"genre_scores_gemma":[0.8755735,0.0002420668,0.1224126,0.0002970483,0.00006917147,0.0003239891,0.0007876914,0.00003855075,0.0002553884],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9326683,"threshold_uncertainty_score":0.3560884,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1945592488696984,"score_gpt":0.4261974346695372,"score_spread":0.2316381857998387,"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."}}