Surgery as an Adjunctive Treatment for Multidrug-Resistant Tuberculosis: An Individual Patient Data Metaanalysis
Bibliographic record
Abstract
BACKGROUND: Medical treatment for multidrug-resistant (MDR)-tuberculosis is complex, toxic, and associated with poor outcomes. Surgical lung resection may be used as an adjunct to medical therapy, with the intent of reducing bacterial burden and improving cure rates. We conducted an individual patient data metaanalysis to evaluate the effectiveness of surgery as adjunctive therapy for MDR-tuberculosis. METHODS: Individual patient data, was obtained from the authors of 26 cohort studies, identified from 3 systematic reviews of MDR-tuberculosis treatment. Data included the clinical characteristics and medical and surgical therapy of each patient. Primary analyses compared treatment success (cure and completion) to a combined outcome of failure, relapse, or death. The effects of all forms of resection surgery, pneumonectomy, and partial lung resection were evaluated. RESULTS: A total of 4238 patients from 18 surgical studies and 2193 patients from 8 nonsurgical studies were included. Pulmonary resection surgery was performed on 478 patients. Partial lung resection surgery was associated with improved treatment success (adjusted odds ratio [aOR], 3.0; 95% confidence interval [CI], 1.5-5.9; I(2)R, 11.8%), but pneumonectomy was not (aOR, 1.1; 95% CI, .6-2.3; I(2)R, 13.2%). Treatment success was more likely when surgery was performed after culture conversion than before conversion (aOR, 2.6; 95% CI, 0.9-7.1; I(2)R, 0.2%). CONCLUSIONS: Partial lung resection, but not pneumonectomy, was associated with improved treatment success among patients with MDR-tuberculosis. Although improved outcomes may reflect patient selection, partial lung resection surgery after culture conversion may improve treatment outcomes in patients who receive optimal medical therapy.
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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 | 0.029 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.061 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".