Bibliographic record
Abstract
The World Health Organization estimates that a third of the world's population is infected with Mycobacterium tuberculosis. Every second, one person becomes newly infected with tuberculosis (TB). In the past two decades, the spread of human immunodeficiency virus infection, worsening poverty and deteriorating health services have resulted in a steady increase in the overall incidence of TB globally. With treatment of latent TB infection (LTBI), the number of infected persons who develop active TB can be significantly diminished. Prevention through treatment of LTBI should therefore be an integral part of the control of TB. Although only a minority of those with LTBI will develop active disease, the risk varies substantially according to the time since infection and medical risk factors. If persons at low risk for TB are selected for preventive chemotherapy, the individual and public health benefits are low, and a large number will have to be treated to prevent a single active case. It is therefore important to identify and treat patients who are at high risk of disease. Tools for rapid and reliable identification of persons with LTBI who are most likely to progress to active disease are urgently needed, as this will permit rational use of preventive treatment by restricting treatment to those patients with the most favourable risk/benefit ratio. The major challenges are efficient identification of those at highest risk of developing disease and ensuring treatment completion with a non-toxic regimen. If these can be overcome, preventive treatment holds the promise to substantially assist in the achievement of global control of TB.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.022 | 0.013 |
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".