Preventing lung function decline and pleural thickening after pleural TB: a systematic review
Bibliographic record
Abstract
BACKGROUND Pleural TB (PTB) is treated with standard anti-TB therapy, but additional treatments may be necessary to mitigate restrictive functional sequelae (RFS). This study identifies effective adjunctive management options to prevent PTB sequelae. METHODS We systematically reviewed studies from PubMed, Cochrane Library, EMBASE, CINAHL, Web of Science, and Ovid, focusing on PTB treatments like oral steroids, pleural drainage, intrapleural therapy, and pleuroscopy. Our primary goal was to assess the impact on pulmonary function test (PFT) results, adhering to Cochrane’s synthesis without meta-analysis (SWiM) guidelines. PROSPERO registry: CRD420251047448. RESULTS 21 out of 1,110 articles met the criteria (8 randomised, 13 nonrandomised). Intrapleural fibrinolysis with urokinase significantly improved predicted forced vital capacity (%FVC) and delta-FVC at 6 months (%FVC increased from 62.6% to 87.2% [ P < 0.01]; delta-FVC = +24.6%; P < 0.01) in free-flowing PTB effusions. No improvements were noted with oral steroids, pleural drainage yielded inconclusive results, and no studies examined pleuroscopy’s effects on PFT. However, both latter interventions demonstrated improvement in other outcomes. CONCLUSION Intrapleural therapies provide robust evidence against RFS in PTB; pleural drainage may help with dyspnoea, while further studies are needed on pleuroscopy.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".