Four months of rifampicin for tuberculosis prevention treatment in children
Bibliographic record
Abstract
We have read with interest the systematic review by Santos et al. in the Journal of Infection and Chemotherapy [ [1] Santos J.M. Fachi M.M. Beraldi-Magalhães F. Böger B. Junker A.M. Domingos E.L. et al. Systematic review with network meta-analysis on the treatments for latent tuberculosis infection in children and adolescents. J Infect Chemother. 2022 Sep 6; 12 (PMID: 36075488, S1341-321X(22)00249-5, In this issue): 1645-1653https://doi.org/10.1016/j.jiac.2022.08.023 Abstract Full Text Full Text PDF Scopus (4) Google Scholar ]. We congratulate the authors for the choice of their subject. Children exposed to tuberculosis are at high risk for the disease and preventive tuberculosis treatment (TPT) of contact children is a high priority for the United Nations [ [2] United NationsPolitical Declaration of the UN General Assembly High-Level Meeting. https://www.who.int/tb/unhlmonTBDeclaration.pdf Google Scholar ] and the World Health Organization [ [3] WHOWHO consolidated guidelines on tuberculosis: module 1: prevention: tuberculosis preventive treatment. https://www.who.int/publications-detail-redirect/who-consolidated-guidelines-on-tuberculosis-module-1-prevention-tuberculosis-preventive-treatment Google Scholar ]. However, we are very concerned by the methods and conclusions of their systematic review.
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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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".