{"id":"W2561606493","doi":"","title":"Lost in Translation: Exposure Misclassification when Relying on Days Supply in Pharmacy Claims Data","year":2014,"lang":"en","type":"dissertation","venue":"TSpace","topic":"Pharmaceutical Practices and Patient Outcomes","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ontario Ministry of Research and Innovation; Canadian Institutes of Health Research; University of Toronto","keywords":"Pharmacy; Translation (biology); Medicine; Data science; Computer science; Family medicine; Biology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.1404365,0.001198341,0.001514424,0.004249568,0.001471378,0.003568394,0.002503387,0.001465802,0.002150428],"category_scores_gemma":[0.4472952,0.0008724229,0.002156146,0.007743202,0.001981833,0.003235459,0.003550577,0.002002477,0.0006049114],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002471425,"about_ca_system_score_gemma":0.003359381,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02704062,"about_ca_topic_score_gemma":0.01501675,"domain_scores_codex":[0.7918483,0.1379615,0.02685978,0.01559382,0.0260087,0.001727899],"domain_scores_gemma":[0.5596741,0.2953868,0.05794278,0.05568085,0.03030949,0.001006104],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0009866703,0.000200612,0.7804517,0.002713614,0.002518158,0.0006152142,0.01013651,0.003625739,0.0009968381,0.009458855,0.01071836,0.1775777],"study_design_scores_gemma":[0.000244467,0.0007087181,0.8478919,0.005302683,0.002419639,0.002071365,0.00667242,0.02301136,0.00798901,0.04450739,0.05880161,0.0003795206],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5664141,0.01675396,0.3530563,0.01660405,0.002799899,0.003264622,0.02141715,0.001025157,0.0186647],"genre_scores_gemma":[0.8948714,0.001989319,0.08298525,0.006879712,0.0007795601,0.002180062,0.007450986,0.0003561263,0.002507512],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8595635,"threshold_uncertainty_score":0.742708,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3285511741140213,"score_gpt":0.4937309970290802,"score_spread":0.1651798229150589,"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."}}