Smoking Cessation: The Role of the Anesthesiologist
Bibliographic record
Abstract
Smoking increases the risk of postoperative morbidity and mortality. Smoking cessation before surgery reduces the risk of complications. The perioperative period may be a "teachable moment" for smoking cessation and provides smokers an opportunity to engage in long-term smoking cessation. Anesthesiologists as the perioperative physicians are well-positioned to take the lead in this area and improve not only short-term surgical outcomes but also long-term health outcomes and costs. Preoperative interventions for tobacco use are effective to reduce postoperative complications and increase the likelihood of long-term abstinence. If intensive interventions (counseling, pharmacotherapy, and follow-up) are impractical, brief interventions should be implemented in preoperative clinics as a routine practice. The "Ask, Advise, Connect" is a practical strategy to be incorporated in the surgical setting. All anesthesiologists should ask their patients about smoking and strongly advise smokers to quit at every visit. Directly connecting patients to existing counseling resources, such as telephone quitlines, family physicians, or pharmacists using fax or electronic referrals, greatly increases the reach and the impact of the intervention.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".