CEASE: A novel patient directed electronic smoking cessation platform for cancer patients.
Bibliographic record
Abstract
6584 Background: Continued smoking in cancer patients receiving treatment results in decreased efficacy, reduced survival, amd increased risk of recurrence. Despite ASCO and AACR policy statements, routine tobacco use screening and provision of smoking cessation treatment has not been widely implemented in the cancer setting. A paper-based tobacco use screening and clinician-dependent referral program for new ambulatory cancer patients was initiated at Princess Margaret Cancer Centre in 2013 resulting in moderate screen rates but low referral rates. In response, we developed and implemented a tailored patient directed electronic smoking cessation platform (CEASE) which included three elements:1) tobacco use assessment tool; 2) patient education on benefits of cessation; 3) a patient directed automatic referral system to smoking cessation programs. Methods: Interrupted time series design to examine the impact of CEASE on process of care (screening rates, referrals offered and accepted) and patient reported (quit attempts, smoking status, uptake of cessation programs) outcomes. Included 20 monthly intervals: 6 pre implementation (Apr-Sept 2015) (PRE), 8 gradual implementation across all tumour sites (Oct 2015-May 2016), and 6 postb implementation (Jun 2016-Nov 2016) (POST). A time series segmented linear regression was conducted to evaluate changes in process of care outcomes (excluding the implementation period). Pre-post self-report patient outcome data was also compared. Results: We assessed data from n = 3785 (PRE) and n = 4726 (POST) new patients. Screening rates increased from 44% using the paper-based approach to 65% with CEASE (p = 0.0019). Referrals offered to smokers who were willing to quit increased from 24% to 100% (p < 0.0001). Accepted referrals decreased from 45% to 26%; though the overall referral rate increased from 11% to 26% (p = 0.0001). The proportion of those using tobacco or attempting to quit did not differ at 3-months. However, engagement with the referral source increased from 4% to 62.5% (p < 0.001). Conclusions: CEASE was successfully implemented across all clinics and resulted in improvements in overall screening and referral rates and engagement with referral services.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | medium |
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".