Patients’ awareness of the surgical risks of smoking
Bibliographic record
Abstract
Objective To describe the smoking patterns of patients receiving elective surgery and their knowledge about the benefits of smoking cessation to inform and strengthen support for patients to quit smoking in order to optimize surgical outcomes. Design Patients who had elective surgery were screened for smoking status, and eligible patients completed a telephone survey. Setting Two regional hospitals in northern British Columbia. Participants Of 1722 patients screened, 373 reported smoking before surgery. Of these, 161 (59.0% women) completed a telephone survey. Main outcome measures Patient smoking cessation, knowledge of the perioperative risks of smoking, use of resources, and health care provider advice and assistance. Results Participants included 66 men and 95 women (mean [SD] age of 51.9 [14.0] years). In total, 7.5% of these patients quit smoking in the 8 weeks before their surgeries, although an additional 38.8% reduced their smoking. Only about half of the patients surveyed were aware that continuing to smoke increased their surgical risks. Further, only half of the patients surveyed reported being advised to quit before their surgeries by a health care professional. Few were using the provincial resources available to support smoking cessation (eg, QuitNow), and 39.6% were unaware of the provincial program to cover the cost of smoking cessation aids (eg, nicotine gum or patches), yet 62.7% of respondents were thinking about quitting smoking. Conclusion Many surgical patients in northern British Columbia who smoked were unaware of the perioperative risks of smoking and the cessation support available to them. An opportunity exists for all health care professionals to encourage more patients to quit in order to optimize their surgical outcomes.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".