Postmortem for Intensified Missed Tuberculosis Case Finding in High Human Immunodeficiency Virus and Tuberculosis-burdened Settings in Sub-Saharan Africa among Adults’ Population: Systematic Review and Meta-analysis
Bibliographic record
Abstract
Tuberculosis (TB) is the leading infectious cause of mortality in sub-Saharan Africa (SSA); the high prevalence of TB in this region is due to human immunodeficiency virus (HIV)-coinfection. Despite the advent of modalities to diagnose TB, undiagnosed TB-related deaths among HIV-infected patients remain significantly high. This systematic review aims at characterizing missed TB cases from postmortem studies. This review informs on the burden of TB missed diagnosis and highlights the need of improving TB case-finding strategies, especially among the high-risk groups and early TB therapy initiation to keeping in with the World Health Organization's end TB strategy. We searched PubMed, Cochrane, Web of Science, and African journals online for studies that looked into missed TB cases following postmortem using the following key terms: postmortem, TB diagnosis, and HIV; we included cross-sectional and cohorts from 1980 in the English language that were carried out in SSA among adults' population. Authors used the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines for reporting, the quality of the included studies was assessed using the Newcastle-Ottawa Scale for observational studies, and STATA 17.0 software was used for analysis. This study was registered in the International Prospective Register of Systematic Reviews with registration number CRD42024507515. The combined prevalence of postmortem missed TB diagnosis among the 6025 participants was 27.13% (95% confidence interval [CI] =14.52-41.89), with a high level of heterogeneity at 98.65% (P < 0.001). The prevalence varied significantly across the included studies, ranging from 1.21% (95% CI = 0.93-1.59) in the general population to 66.67% (95% CI = 50.98-79.37) in people living with HIV (PLWHIV). This current literature suggests that SSA is a region with a high prevalence of missed TB cases but with significant variations between countries. In addition, this study confirms a high number of missed TB infections within the PLWHIV. These results highlight the immediate need for targeted screening and diagnosis strategies and relevant policies.
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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 | 0.011 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.029 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".