Impact of Underweight on the Unsuccessful Treatment Outcome among Adults with Drug-Resistant Tuberculosis: A Systematic Review
Bibliographic record
Abstract
Background: The emergence of drug-resistant (DR) strains of Mycobacterium tuberculosis has disrupted the control of tuberculosis (TB) problem worldwide. Most of DR-TB patients presents with underweight problems. Prior studies showed that body mass index affects sputum conversion and could be a predictor of treatment outcome, but the causal relationship has not been established yet. This systematic review aimed to determine the impact of underweight on the unsuccessful treatment outcome among adults with DR-TB.Methods: Systematic literature search and handsearching were done in four databases: Cochrane, Proquest, Pubmed, and ScienceDirect. Filtering process by using selection criteria yielded 4 eligible articles (2 prospective cohort and 2 retrospective cohort studies) for answering the clinical question. Critical appraisal was conducted by using the Newcastle-Ottawa Quality Assessment Scale (NOS).Results: All four studies were assessed as having high quality according to the NOS score. The findings of all eligible studies were consistent in revealing the impact of underweight on the unsuccessful treatment outcome in DR-TB, with the relative risk: 2.194 (95% confidence interval [CI], 1.134–4.246), 1.771 (95% CI, 1.069–2.931), 3.465 (95% CI, 1.114–2.712), and 4.703 (95% CI, 1.709–12.947), consecutively. The number needed to harm (NNH) of 3–7 indicated the clinically meaningful harm of the exposure. This systematic review showed that one poor outcome incidence could be found with only a few underweight DR-TB patients.Conclusion: Underweight increased the risk of unsuccessful treatment outcome among adults with DR-TB. Low baseline body weight (<40 kg) could be another considerable factor in anticipating the poor treatment outcome.
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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 | 0.013 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".