P137 Early reduction in respiratory readmissions following implementation of a hospital-based stop smoking service
Bibliographic record
Abstract
The NHS Long Term Plan aims to implement tobacco treatment services in all hospitals by 2024, based on the Ottawa Model for Smoking Cessation (OMSC) which has been shown to reduce readmissions and mortality.1 Respiratory patients have a high risk of readmission, with some data showing that almost a quarter were admitted within 30 days of discharge.2 We began implementation of an inpatient Stop Smoking Service at Leeds Teaching Hospitals NHS Trust (LTHT) in November 2022. This was funded by Yorkshire Cancer Research. Current smokers were identified on admission by nursing staff on admission and approached by a Stop Smoking Advisor (SSA) on an opt-out basis. We performed a retrospective audit on respiratory inpatients who were approached by a SSA during the initial 6 months of our programme and any readmissions over a 12-month period were identified via electronic patient records. 182 patients were offered treatment for tobacco dependence. 46% agreed to a supported quit attempt including behavioural intervention and either licensed medication, unlicensed nicotine-containing products or without pharmacotherapy. 54% did not agree to a supported quit attempt. They either declined support, opted for smoking reduction, had supported temporary abstinence, or were already attempting an unsupported quit attempt. 43% of those who accepted a fully supported quit attempt achieved a self-reported 4-week quit. Among patients who accepted a supported quit attempt, 46% were readmitted compared to 51% patients who did not accept a supported quit attempt. This represents a 9% relative risk reduction, or number needed to treat of 21.8 to prevent a readmission (p=0.54). The service has been scaled up across inpatient wards in LTHT. In the first year 1,591 patients have received support and there were 340 4-week quits, of which majority were self-reported. Our data shows that treating tobacco dependence in respiratory inpatients prevents readmissions. This supports the continued implementation of tobacco treatment services in hospitals. References Mullen KA, Manuel DG, Hawken SJ, et al. Effectiveness of a hospital-initiated smoking cessation programme: 2-year health and healthcare outcomes. Tob Control 2017;26:293–9. Stone R, et al. National COPD Audit Programme Outcomes 2014.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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, 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".