Recurrent Tuberculosis in the United States and Canada
Bibliographic record
Abstract
Recurrence of active tuberculosis after treatment can be due to relapse of infection with the same strain or reinfection with a new strain of Mycobacterium tuberculosis. The proportion of recurrent tuberculosis cases caused by reinfection has varied widely in previous studies. We evaluated cases of recurrent tuberculosis in two prospective clinical trials: a randomized study of two regimens for the last 4 months of treatment (n = 1,075) and a study of a twice-weekly rifabutin-containing regimen for human immunodeficiency virus-infected tuberculosis (n = 169). Isolates at diagnosis and from positive cultures after treatment completion underwent genotyping using IS6110 (with secondary genotyping for isolates with less than six copies of IS6110). Of 85 patients having a positive culture after completing treatment, 6 (7.1%) were classified as false-positive cultures by a review committee blinded to treatment assignment. Of the remaining 75 cases with recurrent tuberculosis and genotyping data available, 72 (96%; 95% confidence interval, 88.8-99.2%) paired isolates had the same genotype; only 3 (4%; 95% confidence interval, 0.8-11.2%) had a different genotype and were categorized as reinfection. We conclude that recurrent tuberculosis in the United States and Canada, countries with low rates of tuberculosis, is rarely due to reinfection with a new strain of M. tuberculosis.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".