Cost-effectiveness of point of care smoking cessation interventions in oncology clinics
Bibliographic record
Abstract
BACKGROUND: We examined the cost-effectiveness of providing systematic smoking cessation interventions to oncology patients at point-of-care. METHODS: A decision analytic model was completed from the healthcare payer's perspective and included all incident cancer cases involving patients who smoke in New Brunswick, Canada (n = 1040), cancer site stratifications, and risks of mortality, continued smoking, and cancer treatment failure over one year. Usual care (no cessation support) was compared to the standard Ottawa Model for Smoking Cessation (OMSC) intervention, and to OMSC plus unlimited cost-free stop smoking medication (OMSC + SSM), including nicotine replacement therapy, varenicline, or bupropion. Primary outcomes were incremental cost per quit (ICQ) and incremental cost per cancer treatment failure avoided (ICTFA). RESULTS: The ICQ was $C143 and ICTFA $C1193 for standard OMSC. The ICQ was $C503 and ICTFA was $C5952 for OMSC + SSM. The number needed to treat (NNT) to produce one quit was 9 for standard OMSC and 4 for OMSC + SSM, and the NNT to avoid one first-line treatment failure was 78 for OMSC and 45 for OMSC + SSM. Both were cost-effective in 100% of 1000 simulations. CONCLUSIONS: Given the high clinical benefits and low incremental costs, systematic smoking cessation interventions should be a standard component of first-line cancer treatment.
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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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".