A Web-Based Clinical System for Cohort Surveillance of Specific Clinical Effectiveness and Safety Outcomes: A Cohort Study of Non–Vitamin K Antagonist Oral Anticoagulants and Warfarin
Bibliographic record
Abstract
BACKGROUND: Conventional systems of drug surveillance lack a seamless workflow, which makes it crucial to have an active drug surveillance system that proactively assesses adverse drug events. OBJECTIVE: The aim of this study was to develop a seamless, Web-based workflow for comparing the safety and effectiveness of drugs in a database of electronic medical records. METHODS: We proposed a comprehensive integration process for cohort surveillance using the National Taiwan University Hospital Clinical Surveillance System (NCSS). We studied a practical application of the NCSS that evaluates the drug safety and effectiveness of novel oral anticoagulants (NOACs) and warfarin by cohort tree analysis in an efficient and interoperable platform. RESULTS: We demonstrated a practical example of investigating the differences in effectiveness and safety between NOACs and warfarin in patients with nonvalvular atrial fibrillation (AF) using the NCSS. We efficiently identified 2357 patients with nonvalvular AF with newly prescribed oral anticoagulants between 2010 and 2015 and further developed 1 main cohort and 2 subcohorts for separately measuring ischemic stroke as the clinical effectiveness outcome and intracranial hemorrhage (ICH) as the safety outcome. In the subcohort of ischemic stroke, NOAC users exhibited a significantly lower risk of ischemic stroke than warfarin users after adjusting for age, sex, comorbidity, and comedication in an intention-to-treat (ITT) analysis (P=.01) but did not exhibit a significantly distinct risk in an as-treated (AT) analysis (P=.12) after the 2-year follow-up. In the subcohort of ICH, NOAC users did not exhibit a different risk of ICH both in ITT (P=.68) and AT analyses (P=.15). CONCLUSIONS: With a seamless and Web-based workflow, the NCSS can serve the critical role of forming associations between evidence and the real world at a medical center in Taiwan.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".