New treatment options for multidrug-resistant tuberculosis
Bibliographic record
Abstract
Despite the development of effective treatments, tuberculosis (TB) remains a major health problem. TB continues to infect new victims and kills nearly 2 million people annually. The problem is much greater in resource-limited countries but is present worldwide. Inadequate public health resources, cost, the obligatory long treatment period, and adverse drug effects contribute to treatment failures and relapses. Drug-resistant Mycobacterium tuberculosis (MTB) strains arise spontaneously and are propagated by inadequate treatment. According to World Health Organization global data, 17% of MTB strains in new, previously untreated cases are resistant to at least one drug. Approximately, 3.3% of new MTB cases are resistant to both isoniazid and rifampin, also called multidrug resistant (MDR), and rates of MDR-TB are greater than 60% in previously treated patients in some countries. Approximately 5% of cases of MDR-TB are also resistant to fluoroquinolones and to injectable drugs, and are called extensively drug resistant (XDR). Recently, XDR strains have been isolated that are also resistant to all standard second-line anti-TB medications. Successful drug treatment of TB with complex resistance profiles is virtually impossible with currently available drugs. There is a desperate need for new compounds that cure strains resistant to currently available drugs and for drugs that are better tolerated and will shorten treatment regimens. In the short term, new strategies for the management of drug-resistant TB with currently available drugs are being explored. These include the use of high-dose isoniazid, substitution of rifabutin in a small proportion of rifampin-resistant cases, linezolid, fluoroquinolones, and phenothiazines. A number of novel drugs are undergoing clinical testing and will hopefully be available in the near future. These include the newer oxazolidinones, diarylquinolines, nitroimidazopyrans, ethenylenediamines, pyrroles, and benzothiazinones.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 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".