Utilizing Patient E-learning in an Intervention Study on Preoperative Smoking Cessation
Bibliographic record
Abstract
BACKGROUND: Patients who smoke put themselves at increased risk for serious surgical complications, yet it is not currently routine practice to educate patients about the risk of complications due to smoking. Computer-based smoking cessation programs are increasingly being utilized in the general population and may overcome some of the barriers such as lack of time, knowledge, and training to provide interventions. Our objective was to develop and implement a patient e-learning program designed for surgical patients as part of a multifaceted program aimed at assisting them to quit smoking and to determine the factors cross-sectionally and longitudinally associated with abstinence. METHODS: In this prospective multicenter study, smokers undergoing elective noncardiac surgery participated in a preoperative smoking cessation program consisting of a patient e-learning program, brief advice, educational pamphlet, tobacco quitline referral, letter to the primary care physician, and pharmacotherapy. The patient e-learning program described (1) the benefits of quitting smoking before surgery; (2) how to quit smoking; and (3) how to cope while quitting. The 7-day point prevalence (PP) abstinence on the day of surgery and at 1, 3 and 6 six months after surgery was separately assessed, and factors most associated with abstinence were identified using multivariable logistic regression analysis. Generalized estimating equation methods were used to estimate effect of the factors associated with abstinence longitudinally. The reach of the program was assessed with the number of smokers who participated in the program versus the number of patients who were referred to the program. RESULTS: A total of 459 patients (68.9% of eligible patients) participated. The 7-day PP abstinence at day of surgery, 1 month, 3 months, and 6 months was 22%, 29%, 25%, and 22%, respectively. The variables predicting abstinence at 6 months were use of pharmacotherapy (odds ratio [OR], 7.32; 95% confidence interval [CI], 3.71-14.44; P < .0001) and number of contacts with a tobacco quitline (OR, 1.60; 95% CI, 1.35-1.90; P < .0001). Presence of other smokers in the household (OR, 0.39; 95% CI, 0.21-0.72; P = .0030) and amount spent on cigarettes weekly at baseline (per $10 increase) (OR, 0.73; 95% CI, 0.61-0.87; P = .0004) were barriers to abstinence. CONCLUSIONS: Our preoperative smoking cessation program resulted in a 7-day PP abstinence of 22% at 6 months. A multifaceted intervention including a patient e-learning program may be a valuable tool to overcome some of the barriers to help surgical patients quit smoking.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".