Aggressive Regimens Reduce Risk of Recurrence After Successful Treatment of MDR-TB
Bibliographic record
Abstract
BACKGROUND: We sought to determine whether treatment with a "long aggressive regimen" was associated with lower rates of relapse among patients successfully treated for pulmonary multidrug-resistant tuberculosis (MDR-TB) in Tomsk, Russia. METHODS: We conducted a retrospective cohort study of adult patients that initiated MDR-TB treatment with individualized regimens between September 2000 and November 2004, and were successfully treated. Patients were classified as having received "aggressive regimens" if their intensive phase consisted of at least 5 likely effective drugs (including a second-line injectable and a fluoroquinolone) used for at least 6 months post culture conversion, and their continuation phase included at least 4 likely effective drugs. Patients that were treated with aggressive regimens for a minimum duration of 18 months post culture conversion were classified as having received "long aggressive regimens." We used recurrence as a proxy for relapse because genotyping was not performed. After treatment, patients were classified as having disease recurrence if cultures grew MDR-TB or they re-initiated MDR-TB therapy. Data were analyzed using Cox proportional hazard regression. RESULTS: Of 408 successfully treated patients, 399 (97.5%) with at least 1 follow-up visit were included. Median duration of follow-up was 42.4 months (interquartile range: 20.5-59.5), and there were 27 recurrence episodes. In a multivariable complete case analysis (n = 371 [92.9%]) adjusting for potential confounders, long aggressive regimens were associated with a lower rate of recurrence (adjusted hazard ratio: 0.22, 95% confidence interval, .05-.92). CONCLUSIONS: Long aggressive regimens for MDR-TB treatment are associated with lower risk of disease recurrence.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".