Bibliographic record
Abstract
We thank McBrien et al. for their letter regarding our editorial 1. We agree that establishing a smoking cessation programme in the pre-admission clinic requires considerable effort and interprofessional collaboration. However, we believe this is an important role that anaesthetists should take as peri-operative physicians. There is considerable controversy regarding the use of e-cigarettes for smoking cessation for both the general public and surgical patients. Of note, a recent review of longitudinal studies on e-cigarettes suggests that e-cigarettes are not more effective than other existing pharmacological agents for smoking cessation 2. We did not discuss the use of e-cigarettes in our editorial, as there is currently a lack of published literature on the use of e-cigarettes for peri-operative smoking cessation. Interestingly, a recent American survey reported that a considerable proportion of patients undergoing elective surgery would be willing to use e-cigarettes in the peri-operative period 3. We agree that given the increased use of e-cigarettes, their effectiveness and safety for peri-operative smoking cessation needs to be investigated in well-designed prospective trials.
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 | 0.004 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.048 | 0.043 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".