Exposed, but Not Protected: More Is Needed to Prevent Drug-Resistant Tuberculosis in Healthcare Workers and Students
Bibliographic record
Abstract
"Occupational MDR-TB" … "XDR-TB" … "Treatment-induced hearing loss": 3 life-changing messages imparted over the phone. Three personal accounts are shared highlighting the false belief held by many healthcare workers (HCWs) and students in low-resource settings-that they are immune to tuberculosis despite high levels of occupational tuberculosis exposure. This misconception reflects a lack of awareness of tuberculosis transmission and disease risk, compounded by the absence of accurate occupational tuberculosis estimates. As the global problem of drug-resistant (DR) tuberculosis evolves, HCWs are increasingly infected and suffer considerable morbidity and mortality from occupational DR tuberculosis disease. Similarly, healthcare students are emerging as a vulnerable and unprotected group. There is an urgent need for improved detection, vaccines, preventive therapy, treatment, and support for affected HCWs and those they care for, as well as destigmatization of all forms of tuberculosis. Finally, efforts to protect HCWs and prevent DR tuberculosis transmission by universal implementation of tuberculosis infection control measures should be prioritized.
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".