Improving support for smoking cessation in medical oncology patients: A quality improvement initiative.
Bibliographic record
Abstract
148 Background: Smoking cessation is integral to cancer care. Active smoking is associated with increased toxicity of treatment, poorer response to therapy and is associated with worse overall survival. Patients who quit smoking at diagnosis have better survival outcomes. Cancer Care Ontario has aimed to improve rates of smoking screening and referral to smoking cessation programs based on the validated Ottawa model. Methods: We aimed to implement an “opt-out” referral process for recent or current smokers to a smoking cessation program at the Credit Valley Hospital. We aimed to achieve a referral rate of 20%, based on an institutional baseline of 8.5% and a provincially defined target of 20%. Key stakeholders targeted included nursing, administration, physicians, smoking cessation counsellors and patients. Sequential education interventions were delivered to address gaps in patient and provider knowledge; these included grand rounds, an informal lecture and an educational pamphlet. Results: After the initiative was launched, the referral rate increased from 8.5% to 14.3%. The impact of each intervention is summarized in Table 1. Conclusions: Smoking cessation referrals increased with new process but not to target. Patient refusals lead to a low rate of referral, warranting efforts aimed at addressing patient barriers. Future outcome measures may include smoking cessation rates. [Table: see text]
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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 | 0.013 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".