Post-tuberculosis lung disease: a guide for clinicians
Bibliographic record
Abstract
Post-tuberculosis lung disease (PTLD) is an increasingly recognized condition that significantly affects survivors' quality of life, creating disability and incrementing the risk of mortality. PTLD includes a spectrum of structural and functional lung impairments such as obstructive, restrictive, and mixed patterns, bronchiectasis, and pulmonary fibrosis that persist beyond microbiological cure. Global prevalence data highlight a heavy burden of PTLD, especially in high-incidence regions, driven by late diagnosis and suboptimal treatment. Functional and radiological evaluation remains critical for timely diagnosis, with spirometry and imaging revealing lasting abnormalities in a large proportion of TB survivors. Multidisciplinary care is essential and includes bronchodilator therapy, infections/complications management and prevention, pulmonary rehabilitation, and, in selected cases, surgical intervention. Despite increasing recognition, standardized diagnostic and therapeutic pathways for PTLD are still lacking, and data on optimal follow-up, rehabilitation strategies, and preventive measures remain limited. Prospective studies, better stratification tools, and patient education initiatives are urgently needed to reduce PTLD morbidity and mortality. This narrative review synthesizes current evidence on PTLD epidemiology, clinical evaluation and management while offering practical suggestions for clinicians taking care of people with TB and addressing research needs.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.026 | 0.026 |
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".