Tuberculosis and Tuberculosis/HIV/AIDS-Associated Mortality in Africa: The Urgent Need to Expand and Invest in Routine and Research Autopsies
Bibliographic record
Abstract
Frequently quoted statistics that tuberculosis and human immunodeficiency virus (HIV)/AIDS are the most important infectious causes of death in high-burden countries are based on clinical records, death certificates, and verbal autopsy studies. Causes of death ascertained through these methods are known to be grossly inaccurate. Most data from Africa on mortality and causes of death currently used by international agencies have come from verbal autopsy studies, which only provide inaccurate estimates of causes of death. Autopsy rates in most sub-Saharan African countries have declined over the years, and actual causes of deaths in the community and in hospitals in most sub-Saharan African countries remain unknown. The quality of cause-specific mortality statistics remains poor. The effect of various interventions to reduce mortality rates can only be evaluated accurately if cause-specific mortality data are available. Autopsy studies could have particular relevance to direct public health interventions, such as vaccination programs or preventive therapy, and could also allow for study of background levels of subclinical tuberculosis disease, Mycobacterium tuberculosis-HIV coinfection, and other infectious and noncommunicable diseases not yet clinically manifest. Autopsies performed soon after death may represent a unique opportunity to understand the pathogenesis of M. tuberculosis and the pathogenesis of early deaths after initiation of antiretroviral therapy. The few autopsies performed so far for research purposes have yielded invaluable information and insights into tuberculosis, HIV/AIDS, and other opportunistic infections. Accurate cause-specific mortality data are essential for prioritization of governmental and donor investments into health services to reduce morbidity and mortality from deadly infectious diseases such as tuberculosis and HIV/AIDS. There is an urgent need for reviving routine and research autopsies in sub-Saharan African countries.
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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 | 0.014 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".