Long term follow-up of drug resistant and drug susceptible tuberculosis contacts in a Low incidence setting
Bibliographic record
Abstract
BACKGROUND: Studies examining the transmission of multidrug-resistant tuberculosis (MDR-TB) strains have yielded conflicting results. METHODS: We examined transmission of MDR-TB strains using contact tracing data from a low incidence setting. Contacts of MDR-TB cases diagnosed in British Columbia, Canada, from 1990-2008 were identified through a provincial tuberculosis (TB) registry. Tuberculin skin test (TST) results and TB disease incident rates were determined for contacts. For comparison, TB disease incident rates and TST results were measured in close contacts of isoniazid mono-resistant (HMR-TB) and drug susceptible TB (DS-TB) cases. RESULTS: Of 89 identified close contacts of MDR-TB patients, 5 patients (6%) developed TB disease and 42 (47%) were TST positive. The incidence rate of TB disease (3%, p = 0.31) and TST positivity (49%, p = 0.82) were similar in contacts of HMR-TB cases. Compared with MDR-TB contacts, DS-TB contacts had lower incidence rate of TB disease (2%, p = 0.04) and TST positivity (32%, p < 0.01). All MDR-TB contacts with culture positive TB diagnosed in follow-up were drug-susceptible; three of six HMR-TB contacts with culture positive TB were HMR-TB. Multivariate analysis demonstrated that contact with MDR-TB (adjusted OR 1.72; 95%CI 1.05-2.81) and HMR-TB (adjusted OR 1.99; 95%CI 1.48-2.67) was associated with TST positivity. In addition, adult age, male gender, BCG positivity, source case sputum smear positivity, foreign birth and fewer contacts per source case were significantly associated with TST positivity in the multivariate model. CONCLUSION: Contacts of MDR-TB and HMR-TB patients in a low incidence setting show high rates of TST positivity and TB disease but low rates of drug resistance.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".