A retrospective survey of the use of bupropion slow release by members of the Canadian Armed Forces.
Bibliographic record
Abstract
BACKGROUND: Since the addition of bupropion slow release (Zyban, GlaxoSmithKline, Mississauga) to the Department of National Defence (DND) smoking cessation program (Butt Out), utilizing bupropion (Zyban) in combination with behaviour modification counselling, the Directorate of Medical policy has received several anecdotal reports from pharmacists and Canadian Forces members attributing significant side effects to the use of Zyban. As a result, the DND wished to assess the benefits versus the risks of using Zyban as part of the smoking cessation program. Subsequently, a retrospective review of the use of Zyban within the Canadian Forces over a one-year period was solicited to assess current policies. METHODS: Surveys were sent to Canadian Forces members receiving Zyban between September 1, 1998 and August 31, 1999. Members were questioned about smoking history and current status, perceived effectiveness of bupropion and both positive and negative experiences with the drug. Those reporting side effects and who had consented were contacted for an interview to obtain further details and information regarding the use of medical resources and effects on job performance. Members of the Canadian Forces visiting a doctor due to side effects were asked for permission to review their medical charts. RESULTS: Zyban was dispensed to approximately 1171 members over the one-year period and 357 responded to the survey. The point prevalence smoking cessation rate was 47% at the time of the survey and ex-smokers had been smoke-free for a mean of 181 days. Approximately 91% of ex-smokers and 52% of smokers found Zyban helpful in quitting. Side effects were reported by 252 members and 160 interviews were completed. Forty-three interviewees had seen a doctor because of side effects. Sixteen of the 43 charts were audited. Fifty-two respondents stated that side effects affected their ability to do their primary job. Two individuals were hospitalized for a total of six days. CONCLUSIONS: In light of the demonstrated effectiveness of Zyban and the overwhelming health benefits associated with smoking cessation, it is recommended that the current policies of funding for the DND smoking cessation program be left in place. The impact of Zyban's side effects on job performance and medical resources should be minimized through close monitoring and Zyban prescriptions should be dispensed in two-week quantities.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".