All editorial matter in CMAJ represents the opinions of the authors and not necessarily those of the Canadian Medical Association. CMAJ•JAMC Commentary
Bibliographic record
Abstract
Smoking is the single most preventable cause of deathand disability. The World Health Organization esti-mates that, around the globe, 1.3 billion smokers pur-chase 10 million cigarettes every minute, and that every 8 seconds somebody dies from a tobacco-related disease. If current trends continue, smoking will kill 1 in 6 people worldwide.1 The primary prevention of disease attributable to smoking requires effective treatment for the ultimate vec-tor of this epidemic: tobacco dependence. Several pharma-cotherapies have proven to be efficacious for the treatment of tobacco dependence. However, critical to the current and future success of tobacco control efforts is the dissemination of interventions from clinical trials to the broad population of tobacco users. Unfortunately, widespread dissemination of effective tobacco interventions remains elusive. In this issue of CMAJ, Eisenberg and colleagues2 report the results of their meta-analysis of pharmacotherapies for the
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 | 0.004 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.013 | 0.011 |
| Insufficient payload (model declined to judge) | 0.091 | 0.041 |
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".