Therapeutic drug monitoring in anti-tuberculosis treatment: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Therapeutic drug monitoring (TDM) may improve tuberculosis (TB) treatment outcomes, but there is little evidence to guide TDM in clinical practice. DESIGN: We performed a systematic review and meta-analysis to summarise existing literature on TDM in first-line drugs. RESULTS: We identified 41 studies that reported 2 h post-dose drug concentrations (C2h) for first-line drugs and 12 studies that reported clinical outcomes. We pooled data by study quality, design, region, dosing modality and patient characteristics. The pooled proportion of subjects with low isoniazid C2h was 0.43 (95%CI 0.32-0.55), 0.67 (95%CI 0.60-0.74) had low rifampicin C2h, 0.27 (95%CI 0.17-0.38) had low ethambutol C2h, and 0.12 (95%CI 0.07-0.19) had low pyrazinamide C2h. Patients with diabetes had a non-significant increase in the proportion of subjects with low C2h levels across all four drugs. Only three of 12 studies that examined clinical outcomes demonstrated an association between low C2h and unsuccessful treatment outcomes. CONCLUSION: Across a wide variety of studies, a high proportion of patients undergoing first-line anti-tuberculosis treatment had 2 h drug concentrations below the accepted normal threshold. These findings point to a discrepancy between accepted 2 h TDM thresholds and TB drug dosing recommendations.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".