Nicotine addiction management following surgery: a quality improvement approach in the post anesthesia care unit
Bibliographic record
Abstract
QUALITY PROBLEM: For smokers, hospital admission is accompanied by forced involuntary nicotine abstinence due to smoke-free site/grounds policies. An audit of patients admitted to our surgical wards revealed that identification of smoking status was inadequate and that nicotine addiction management (NAM) was infrequently offered. The project aimed to enhance both these metrics by initiating NAM in the post anesthesia care unit (PACU). INITIAL ASSESSMENT: Out of 744 patients admitted to our PACU in August 2015, 54% had their smoking status documented. The 200 patients (27%) out of the 744 were smokers and only 50% were offered NAM before discharge. CHOICE OF SOLUTION: PACU unit staff to determine the smoking status of every patient before discharge from the PACU (later changed to OR nursing staff) and, if a patient was identified as a smoker, to offer NRT (patch and mouth spray only) and initiate therapy prior to transfer of the patient to the ward. IMPLEMENTATION: Data about number of patients admitted, presence of documented smoking status, number of identified smokers, and number offered/accepted nicotine replacement therapy (NRT) were collected at baseline and thereafter quarterly. Engaging video education sessions addressed the education gaps highlighted in a needs assessment. Identification of smoking status was made part of preoperative checklist and NRT was made available in post-operative recovery room. RESULTS: These interventions resulted in an increase in screening for tobacco use from 54% at baseline to 95% and the offer of NRT to smokers from 50 to 89%.
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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 | 0.021 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".