New Drugs for the Treatment of Tuberculosis: Needs, Challenges, Promise, and Prospects for the Future
Bibliographic record
Abstract
For the first time in 40 years, a portfolio of promising new compounds for the treatment of tuberculosis is on the horizon. The introduction of new drugs in combination treatment for all forms of tuberculosis raises several issues related to patients' access to novel treatments, programmatic feasibility, cost effectiveness, and implications for monitoring and surveillance, particularly with regard to the development of drug resistance. Particular attention should be given to the identification of optimal drug combination(s) for the treatment of all forms of tuberculosis, particularly in high-risk and vulnerable groups, such as human immunodeficiency virus-coinfected persons and children, and to the rational use of new drugs. Addressing these issues adequately requires the establishment of clear guidelines to assist countries in the development of policies for the proper use of tuberculosis drugs in a way that guarantees access to best treatments for all those in need and avoids inappropriate use of new drugs. After a description of these various challenges, we present activities that will be carried out by the World Health Organization in collaboration with key stakeholders for the development of policy guidelines for optimal treatment of tuberculosis.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".