Patterns of lapses and recoveries during a quit attempt using varenicline and behavioral counseling among smokers with and without HIV.
Bibliographic record
Abstract
Addressing tobacco use among HIV+ smokers is a priority. Lack of knowledge about how HIV+ smokers respond to tobacco use treatments limits our ability to effectively treat this population of smokers. Using data from 2 clinical trials that provided 12 weeks of varenicline and behavioral counseling, 1 with smokers with HIV (n = 89) and 1 with smokers without HIV (n = 179), we used mixed logistic regression modeling to compare point-prevalence abstinence rates and adherence to the initial target quit date (TQD) and Cox regression for repeated outcomes to evaluate lapse and recovery dynamics between the groups. Sixty percent of HIV- smokers refrained from smoking at the TQD while only 33% of HIV+ smokers did (odds ratio [OR] = 0.32, 95% CI [0.18, 0.56], p < .001). The point-prevalence abstinence rates at Week 12 were 31% (HIV-) and 28% (HIV+; OR = 0.7, 95% CI [0.42, 1.16], p = .16) and the point prevalence abstinence rates at Week 24 were 22% (HIV-) and 15% (HIV+; OR = 0.87, 95% CI [0.49, 1.57], p = .65). Although there was no interaction between HIV status and lapse risk, χ2(3) < 1, there was a significant interaction for the recovery model, (χ2(3) = 20.4, p < 0.001): as the number of events increased, the time to the next recovery became longer among smokers with HIV, compared to smokers without HIV. Although HIV+ smokers were treated effectively with varenicline, compared to HIV- smokers, they showed significantly lower initial cessation at the TQD and took increasingly longer to recover following lapses. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".