Lethal interaction: the colliding epidemics of tobacco and tuberculosis
Bibliographic record
Abstract
Tobacco consumption ranks high among the leading health risks and tuberculosis (TB) is a major public health issue in countries where the smoking problem has reached epidemic proportions. Given that both smoking and TB are major health concerns and are widely prevalent in several countries, it is surprising that the association between smoking and TB is still a matter of debate and controversy. Although several studies have evaluated the effect of smoking on TB, the association has been largely overlooked by the TB and public health communities at large. Three recent reviews, including two meta-analyses, have summarized a large body of published literature on the association between smoking and various TB outcomes. These reviews show that there is considerable evidence that tobacco smoking is associated with TB. The evidence is strong for TB disease but less strong for TB infection and mortality. Even if the effect is relatively modest, the population-attributable risk is likely to be substantial due to the widespread nature of tobacco exposure. TB control programs must begin to address tobacco control as a potential preventive intervention. Since tobacco control will have multiple health benefits, it is likely to be a highly cost-effective intervention from a societal perspective.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".