Effect of smoking history on outcome of patients diagnosed with TB and HIV
Bibliographic record
Abstract
Tobacco use, infection with HIV and active tuberculosis (TB) are important public health problems worldwide. Smoking affects susceptibility to TB, with an increased risk of infection, TB disease and TB death [1, 2]. An estimated 1.1 million of the 8.6 million people who developed TB in 2012 were HIV-positive [3]. It has been estimated that smoking could cause 18 million excess cases of TB and 40 million excess deaths between 2010 and 2050 [4], but little is known about the effect of smoking on the outcomes of people receiving care for both HIV and TB. Among TB–HIV patients on antiretrovirals those who smoke(d) are more likely to have adverse TB treatment outcomes The authors thank the participating healthcare workers. Without their enthusiasm and dedication, this work could not have been carried out. We thank S. Mpuate (School of Public Health, University of Kinshasa, Kinshasa, DR Congo) and E. Cromwell (Dept of Epidemiology, University of North Carolina, Chapel Hill, NC, USA) for assistance in preparing the ITART dataset and J. Bettinger (Vaccine Evaluation Center, University of British Columbia, Vancouver, BC, Canada) for helpful discussions.
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 | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".