Prevalence of inappropriate tuberculosis treatment regimens: a systematic review
Bibliographic record
Abstract
A potential threat to the success of new tuberculosis (TB) drugs is the development of resistance. Using drugs in appropriate regimens, such as those recommended in the World Health Organization (WHO) treatment guidelines, prevents the development of resistance. We performed a systematic review to assess the prevalence of inappropriate prescription of TB drugs for the treatment of TB. MEDLINE, EMBASE and other databases were searched for relevant articles in January 2011. Observational studies published from 2000 that included TB patients receiving treatment were selected. A treatment regimen was considered inappropriate if the regimen was not a WHO recommended regimen. 37 studies were included. Inappropriate treatment regimens were prescribed in 67% of studies. The percentage of patients receiving inappropriate regimens varied between 0.4% and 100%. In 19 studies the quality of treatment regimen reporting was low. Despite the fact that assessment of inappropriate treatment was hampered by low quality of reporting, our data indicate a reasonable amount of inappropriate prescription of TB treatment regimens. Thus, there is a risk that new drugs will be used in inappropriate treatment regimens, even with WHO guidelines in place, introducing the risk of resistance development. This article highlights the need to improve implementation of the WHO treatment of TB guidelines.
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 | 0.009 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.011 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".