Identifying sequential episodes of pharmacotherapy as a method for assessing treatment failure in comparative effectiveness research
Bibliographic record
Abstract
PURPOSE: To describe and implement a novel method of measuring comparative effectiveness using sequential episodes of pharmacotherapy as a proxy for treatment failure. METHODS: Retrospective cohort study using linked deidentified data from the British Columbia Ministry of Health during a government-sponsored smoking cessation reimbursement program.Three study cohorts were created based on first use of varenicline, bupropion, or nicotine replacement therapy (NRT), for adults aged 18 or older, in the period September 30th, 2011 to March 31st, 2013. The study cohorts were analyzed for sequential episodes of pharmacotherapy, defined as re-initiating a smoking cessation pharmacotherapy after an initial episode of treatment and washout period. The statistical analysis used propensity score adjusted log-binomial regression models with one-year and two-year fixed follow-up after a 12-week washout period. A sensitivity analysis excluded the washout period. A secondary analysis investigated predictors of receiving a sequential episode of smoking cessation pharmacotherapy RESULTS: 116,442 participants of the B.C. Smoking Cessation Program were analyzed. Compared to NRT, varenicline users were 13% less likely, and bupropion users were 18% less likely, to re-start smoking cessation therapy within 1-year after an initial course of treatment. CONCLUSIONS: Sequential episodes of pharmacotherapy identified treatment failures to smoking cessation therapy. Based on sequential episodes of pharmacotherapy during a drug benefit policy of smoking cessation medications, varenicline and bupropion were more effective aids to smoking cessation than NRT. The method was also used to identify patient characteristics associated with treatment effectiveness.
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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 | 0.602 | 0.728 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.010 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".