Self-Reported Risks for Multiple-Drug Resistance among New Tuberculosis Cases: Implications for Drug Susceptibility Screening and Treatment
Bibliographic record
Abstract
BACKGROUND: Multiple drug-resistance in new tuberculosis (TB) cases accounts for the majority of all multiple drug-resistant TB (MDR-TB) worldwide. Effective control requires determining which new TB patients should be tested for MDR disease, yet the effectiveness of global screening recommendations of high-risk groups is unknown. METHODS: Sixty MDR-TB cases with no history of previous TB treatment, 80 drug-sensitive TB and 80 community-based controls were recruited in Lima, Peru between August and December, 2008 to investigate whether recommended screening practices identify individuals presenting with MDR-TB. Odd ratios (OR) and 95% confidence intervals (CI) were calculated using logistic regression to study the association of potential risk factors with case/control variables. RESULTS: MDR-TB cases did not differ from drug-sensitive TB and community controls in rates of human immunodeficiency virus infection, reported hospital or prison visits in the 3 years prior to diagnosis. MDR-TB cases were more likely than drug-sensitive TB controls to have had a recent MDR-TB household contact (OR 4.66, (95% CI 1.56-13.87)); however, only 15 cases (28.3%) reported this exposure. In multivariate modeling, recent TB household contact, but not contact with an MDR-TB case, remained predictive of MDR-TB, OR 7.47, (95% CI 1.91-29.3). Living with a partner rather than parents was associated with a lower risk of MDR-TB, OR 0.15, (95% CI 0.04-0.51). CONCLUSION: Targeted drug susceptibility testing (DST) linked to reported MDR-TB contact or other high-risk exposures does not identify the majority of new TB cases with MDR disease in Lima where it is endemic. All new TB cases should be screened with DST to identify MDR patients. These findings are likely applicable to other regions with endemic MDR-TB.
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".