Long-term effects of coarse particulate matter and modifications by temperature and relative humidity on all-cause mortality in a MDR-TB cohort
Bibliographic record
Abstract
<bold>Background:</bold> The association between ambient coarse particulate matter (PM2.5-10) and mortality in MDR-TB patients has not yet been studied. The modifying effects of temperature and humidity on this association are completely unknown. This study aims to evaluate the effects of long-term PM2.5-10 exposures, and their modifications by temperature and humidity on mortality among MDR-TB patients. <bold>Methods:</bold> A Chinese cohort of 3,469 MDR-TB patients was followed up from diagnosis until death, loss to follow-up, or the study’s end, averaging 2,567 days per patient. PM2.5-10 concentrations were derived from the difference between PM10 and PM2.5. Cox proportional hazard models estimated hazard ratios (HRs) per 3.74 μg/m3 (IQR) exposure to PM2.5-10 and mortality for the full cohort and individuals at distinct long-term and short-term temperature and humidity levels, adjusting for other air pollutants and potential covariates. Exposure-response relationships were quantified using smoothed splines. <bold>Results:</bold> Hazard ratios of 1.733 (95% CI, 1.407, 2.135) and 1.427 (1.114, 1.827) were observed for mortality in association with PM2.5-10 exposures for the full cohort under long-term and short-term exposures to temperature and humidity. Modifying effects by temperature and humidity were heterogenous across sexes, age, treatment history, and environment. Nonlinear exposure-response curves suggested a cumulative risk of PM2.5-10-related mortality starting from a low exposure concentration around 15 μg/m3. <bold>Conclusion:</bold> Immediate surveillance of PM2.5-10 is crucial to mitigate the progression of MDR-TB severity due to co-exposures.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".