Assisted Reproductive Therapy (ART) Outcomes in Women with a History of Tuberculosis: Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Genital tuberculosis (TB) is a significant cause of infertility in high disease burden countries. Assisted Reproductive Therapy (ART) success in this population remains unclear. Objectives: To conduct a systematic review and meta-analysis to assess ART outcomes in women with a history of TB. Search Strategy: Studies were identified through PubMed, Embase, Global Health and Cinhal. Selection Criteria: Studies reporting ART outcomes in women with a history of TB, compared to women without TB, were included. Studies without a non-TB comparison group were excluded. Data Collection and Analysis: Four authors independently screened articles. Risk of bias was assessed with the Newcastle-Ottawa Score. Meta-analysis was performed with random-effects models to compute odds of clinical pregnancy, miscarriage, live birth and mean differences in oocytes retrieved. Main Results: Of 1184 records identified, 12 studies were included. Ten studies underwent meta-analysis, comprising 3,532 TB patients and 9,163 non-TB patients. Women with TB had significantly lower odds of clinical pregnancy (odds ratio [OR] 0.82, 95% CI [0.67, 1.01], p=0.06) and live births (OR 0.74, 95% CI [0.61, 0.90], p=<0.001). The odds of miscarriage (OR 1.37, 95% CI [0.91, 2.05], p=0.13) was higher in TB patients with a trend towards significance. Mean number of oocytes retrieved (mean difference -0.20, 95% confidence interval CI [-1.32, 0.92], p=0.73) was not different between the groups. Conclusions: ART outcomes are poorer in women with a history of TB. Variability in study quality and bias suggests the need for multi-centre studies with standardized reporting of ART outcomes and TB treatment.
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.036 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".