Efficacy of a smoking‐cessation intervention for elective‐surgical patients
Bibliographic record
Abstract
We tested an intervention to help smokers abstain (fast) from smoking before surgery, maintain abstinence postoperatively, and achieve long-term cessation. A randomized experiment included 237 patients admitted for presurgical assessment who smoked. The intervention included counseling and nicotine replacement therapy. Treatment group participants (73.0%) were more likely to fast than were controls (53.0%): chi(2)(1, N = 228) = 8.89, p =.003, and more likely to be abstinent 6 months after surgery (31.2% vs. 20.2%). There was no significant difference in the abstinence rates at 12 months after surgery, chi(2)(1, N = 169) <.001, p = 1.00. Encouraging patients to fast from smoking before surgery and postoperative support are efficacious ways to reduce preoperative and immediate post-operative tobacco use.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".