PERIOPERATIVE SMOKING CESSATION
Bibliographic record
Abstract
This thesis is concerned with the issue of smoking in the surgical population. Globally, over 300 million adults undergo surgery annually and around 60 million are smokers. The population impact of this is enormous. Moreover, smoking increases postoperative complications and is a leading cause of morbidity and mortality. Smoking rates are declining in the general population; but remains higher among surgical populations. Despite being an important modifiable risk factor, and the availability of treatments for smoking, many patients presenting for surgery still smoke and many resume smoking after surgery. Importantly, unfortunately in surgical settings, the implementation of evidence-based cessation interventions is still suboptimal. The time around surgery is a “teachable moment” and surgical guidelines recommend that all patients who smoke should be provided with evidence-based smoking cessation assistance. This thesis seeks to answers the following questions: What are the factors that determine abstinence from smoking after surgery? What is (are) the best smoking cessation intervention(s) in the surgical setting? What factors constitute barriers and facilitators to the implementation of effective smoking cessation interventions? Is cytisine effective for smoking cessation? Will the use of cytisine and behavioral counselling delivered via personalized video messaging increase abstinence from smoking at 6 months post-randomization among surgical patients? Using a variety of research methodologies, the data provided across the 5 papers in this thesis inform these knowledge gaps. Chapter 1 is an introduction providing the rationale for conducting each of the included studies. Chapter 2 is a secondary analysis from the Vascular events in Noncardiac Surgery Patients Cohort Evaluation (VISION) study that evaluated the determinants of smoking abstinence in a representative sample of patients undergoing major non-cardiac surgery. Chapter 3 is a systematic review, pairwise meta-analysis, and network meta-analysis of randomized controlled trials evaluating preoperative smoking cessation interventions. Chapter 4 is a scoping review that explores the barriers and facilitators to smoking cessation in the surgical setting. Chapter 5 is a systematic review and meta-analysis of cytisine for smoking cessation Chapter 6 reports on the rationale and design of the PeRiopEratiVE smokiNg CessaTion (PREVENT) randomized controlled trial evaluating the efficacy and safety of cytisine versus placebo, and in a 2x2 factorial, personalized video messaging versus standard care for smoking cessation among adults undergoing surgery. Chapter 7 is the conclusion chapter wherein I discuss the key findings, limitations, and implications of the research presented in this PhD thesis.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.037 | 0.013 |
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".