Uptake of prescription smoking cessation pharmacotherapies after hospitalization for major cardiovascular disease
Bibliographic record
Abstract
AIMS: We determined the prevalence of prescription smoking cessation pharmacotherapy (SCP) use after hospitalization for major cardiovascular disease (MCD) among people who smoke and whether this varies by sex. METHODS AND RESULTS: We conducted a population-based cohort study including all people hospitalized in New South Wales, Australia, between July 2013 and December 2018 (2017 for private hospitals) with an MCD diagnosis. For patients who also had a diagnosis of current tobacco use, we used linked pharmaceutical dispensing records to identify prescription SCP dispensings within 90 days post-discharge. We determined the proportion who were dispensed an SCP within 90 days, overall and by type of SCP. We used logistic regression to estimate the odds of females being dispensed an SCP relative to males. Of the 150 758 patients hospitalized for an MCD, 20 162 (13.4%) had a current tobacco use diagnosis, 31% of whom were female. Of these, 11.3% (12.4% of females, 10.9% of males) received prescription SCP within 90 days post-discharge; 3.0% were dispensed varenicline, and 8.3% were dispensed nicotine replacement therapy patches. Females were more likely than males to be dispensed a prescription SCP [odds ratio (OR) 1.16, 95% confidence interval (CI) 1.06-1.27)]; however, this was not maintained after adjusting for potential confounders (adjusted OR 1.04, 95% CI 0.94-1.15). CONCLUSION: Very few females and males who smoke use prescription SCPs after hospitalization for an MCD. The use of varenicline, the SCP with the highest efficacy, was particularly low. This represents a missed opportunity to increase smoking cessation in this high-risk population, thereby reducing their risk of recurrent cardiovascular events.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".