{"id":"W1569938899","doi":"10.1002/pds.2324","title":"Challenges in the design and analysis of sequentially monitored postmarket safety surveillance evaluations using electronic observational health care data","year":2012,"lang":"en","type":"article","venue":"Pharmacoepidemiology and Drug Safety","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":55,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Centers for Disease Control and Prevention; Hamilton Health Sciences Foundation; U.S. Department of Health and Human Services","keywords":"Observational study; Medicine; Interim analysis; Type I and type II errors; Confounding; Interim; Sample size determination; Pharmacoepidemiology; Patient safety; Population; Randomized controlled trial; Research design; Health care; Clinical study design; Clinical trial; Statistics; Data mining; Computer science; Internal medicine; Environmental health","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.06567924,0.0001759038,0.0009898944,0.000113525,0.0001742214,0.00000485067,0.0003341929,0.0001055179,0.00004204273],"category_scores_gemma":[0.04499788,0.0001295518,0.00007165997,0.0003764214,0.0002306116,0.0001344939,0.0001692271,0.0003528004,3.639416e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008280393,"about_ca_system_score_gemma":0.0002067157,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008093452,"about_ca_topic_score_gemma":0.0001016702,"domain_scores_codex":[0.9738587,0.02365359,0.001352157,0.0004341718,0.0002187857,0.0004825674],"domain_scores_gemma":[0.8510298,0.1477806,0.0005135302,0.0004774023,0.0001044473,0.0000942046],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.003125406,0.0006792438,0.5459879,0.0008373988,0.004328573,0.000002610136,0.009037561,0.002721315,0.0002254475,0.2961659,0.001703248,0.1351855],"study_design_scores_gemma":[0.001506239,0.00006811932,0.7956821,0.00004583581,0.001469682,0.000009593856,0.0007032827,0.08284231,0.00001381882,0.116667,0.0007191389,0.0002729304],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0562089,0.06089255,0.8554702,0.02223732,0.0006549924,0.002243609,0.001964858,0.0000553159,0.0002722123],"genre_scores_gemma":[0.5833056,0.0141676,0.4014641,0.0007275838,0.0001583006,0.00001950752,0.000141761,0.00001348156,0.000002018492],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5270967,"threshold_uncertainty_score":0.9630465,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.8308158335019783,"score_gpt":0.642883352138763,"score_spread":0.1879324813632153,"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."}}