Peer Review #1 of "E-cigarettes versus nicotine patches for perioperative smoking cessation: a pilot randomized trial (v0.1)"
Bibliographic record
Abstract
Introduction: Cigarette smoking by surgical patients is associated with increased complications.Ecigarettes have emerged as a potential smoking cessation tool.We sought to determine the feasibility and acceptability of e-cigarettes, compared to nicotine patch, for perioperative smoking cessation in veterans.Methods: Preoperative patients were randomized to either the nicotine patch group (n=10) or the ecigarette group (n=20).Both groups were given a free 6-week supply in a tapering dose.All patients received brief counseling, a brochure on perioperative smoking cessation, and referral to the California Smokers' Helpline.The primary outcome was rate of smoking cessation on day of surgery confirmed by exhaled carbon monoxide.Secondary outcomes included smoking habits, pulmonary function, adverse events, and satisfaction with the products on day of surgery and at 8-weeks follow-up.Results: Biochemically verified smoking cessation on day of surgery was similar in both groups.Change in forced expiratory volume in one second (FEV1) was 592ml greater in the e-cigarette group (95% CI 153-1031ml, p=0.01) and change in forced expiratory volume in one second to forced vital capacity ratio (FEV1/FVC ratio) was 40.1% greater in the e-cigarette group (95% CI 18.2%-78.4%,p=0.04).Satisfaction with the product was similar in both groups.Discussion: E-cigarettes are a feasible tool for perioperative smoking cessation in veterans with quit rates comparable to nicotine replacement patch.Spirometry appears to be improved 8-weeks after initiating e-cigarettes compared to nicotine patch, possibly due to worse baseline spirometry and more smoking reduction in the END group.An adequately powered study is recommended to determine if these results can be duplicated.
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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 | 0.012 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.162 | 0.019 |
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".