The 2003 Ed Nelson Lecture. Smoking Cessation Revisited. Is It Time to Change Our Approach?
Bibliographic record
Abstract
linical approaches to smoking cessation are based on a goal of increasing the odds that a given person will quit smoking. Current treatment recommendations are based on metaanalyses of randomized clinical trials conducted over short intervals. An underlying assumption is that nicotine is the primary reason it is so difficult to quit and that pharmacotherapies are the “best” way to treat the underlying dependency. Data on the relative risk of developing tobacco related disease is used as the basis for selecting priority sub-populations (e.g., those who smoke the most). But should these data be used as the foundation for creating national strategies for the treatment of smokers? The presenter will use data from Canada to offer a different view. He will argue that national strategies should not be simple extrapolations of clinical treatments or relative risk ratios. He will argue that national strategies must employ different goals and outcome measures. For example, our aim should be to use available resources to maximally reduce the health and economic burden of smoking. Policies that create supportive environments, communication campaigns that build self-awareness and efficacy, combined with a triage system that matches smokers to different types of treatment should be the foundation of our approach. Traditional clinical approaches (especially pharmacotherapy) should be used selectively rather than universally.
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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.013 | 0.033 |
| Insufficient payload (model declined to judge) | 0.015 | 0.009 |
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".