Whole Blood Mycobacterial Growth Assays for Assessing Human Tuberculosis Susceptibility: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: tuberculosis susceptibility. Methods: A systematic search was done for studies assessing associations between WBMGA results and tuberculosis susceptibility. Meta-analyses were performed for eligible studies by calculating population-weighted averages. Results: No studies directly assessed whether WBMGA results predicted tuberculosis susceptibility. 15 studies assessed associations between WBMGA results and proven correlates of tuberculosis susceptibility, which we divided in two categories. Firstly, WBMGA associations with factors believed to reduce tuberculosis susceptibility were statistically significant in all eight studies of: BCG vaccination; vitamin D supplementation; altitude; and HIV-negativity/therapy. Secondly, WBMGA associations with probable correlates of tuberculosis susceptibility were statistically significant in three studies of tuberculosis disease, in a parasitism study and in two of the five studies of latent tuberculosis infection. Meta-analyses for associations between WBMGA results and BCG vaccination, tuberculosis infection, tuberculosis disease and HIV infection revealed consistent effects. There was considerable methodological heterogeneity. Conclusions: The study results generally showed significant associations between WBMGA results and correlates of tuberculosis susceptibility. However, no study directly assessed whether WBMGA results predicted actual susceptibility to tuberculosis infection or disease. We recommend optimization and standardization of WBMGA methodology and prospective studies to determine whether WBMGA predict susceptibility to tuberculosis disease.
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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 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.023 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".