A pilot randomized trial of a virtual perioperative smoking cessation bundle in a tertiary care hospital
Bibliographic record
Abstract
Background: Smokers have increased complications after surgery. We sought to study the feasibility of a structured virtual smoking cessation bundle. Methods: We conducted a pilot parallel randomized controlled trial involving adult smokers scheduled for elective surgery 1 or more weeks after enrolment. The intervention bundle consisted of an emailed video and brochure, QuitNow referral, and pharmacy referral. The control group received usual care of uncoordinated advice on smoking cessation. Perioperative caregivers, but not participants, were blinded to group allocation. The primary outcome was the participant’s self-reported uptake of smoking cessation information and available resources. Results: We enrolled the target 30 patients (15 intervention and 15 control). The recruitment rate was 0.8 patients/wk; 59% (30/51) of all eligible patients were enrolled. By day of surgery, 1 patient withdrew from the study and 1 was lost to follow-up. The median (interquartile range [IQR]) number of smoking cessation resources used was higher in the intervention group than in the control group on the day of surgery (1 [IQR 1 to 3] v. 0 [IQR 0–1], p = 0.002), 30 days after surgery (1 [IQR 0 to 2] v. 0 [IQR 0 to 0], p = 0.01), and 8 weeks after randomization (1 [IQR 0 to 1] v. 0 [IQR 0 to 0], p = 0.003), but not different at 6-month follow-up (1 [IQR 0 to 1] v. 0 [IQR 0 to 0], p = 0.1). Conclusion: Patients in the intervention group reported more use of smoking cessation resources than those in the control group. This pilot trial demonstrated that a virtually delivered preoperative smoking cessation bundle was feasible and acceptable to patients. Trial registration: ClinicalTrials.gov, no. NCT04487548.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".