Systematic Follow-Up of a Cohort of Smokers Who Received a Standard Smoking Cessation Intervention with Soft Laser Therapy
Bibliographic record
Abstract
Background:Different techniques may help smokers quitting smoking. Recently soft laser treatment (SLT) has been used with reported not documented high success rates. We decided to conduct a systematic assessment of the charts of subjects treated at one SLT clinic in Montreal.Method: Historical cohort: Subjects treated by SLT during the period May to September 2005. Primary outcome: "not smoking" 6 months after the intervention. The intervention included SLT supported by complementary natural products for comfort. 84 points were treated according to auriculotherapy principles. Sample size was calculated for precision around success rate. Statistical analysis was intent to treat (ITT) and per protocol.Results:Primary outcome: proportion of success at 6 months were 117/210 (44%), 95% CI: 38% - 51% with ITT approach and 93/167 (56%), 95% CI: 48% - 63% with per protocol analysis. Results at 12 months were 40% (33% - 46%) and 62% (54% - 70%) for the two approaches, respectively.Discussion:Our results suggest a possible effect of the SLT that would be in the high range of those observed with other interventions. However, absence of control group and other limitations prevent making any conclusion. Double blind placebo controlled randomized trials need to be conducted.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".