Bibliographic record
Abstract
The primary objective of this study was to examine the role of diabetes in the transmission dynamics of Mycobacterium tuberculosis. We conducted a molecular epidemiologic study of 1549 TB cases between January 1996 and November 2007 in Montreal. No significant association was found between diabetic status and cluster membership defined by shared strains. Diabetic TB patients did not generate more subsequent cases compared to non-diabetics (adj. relative transmission index 0.8, 95% CI 0.1-4.6). When tuberculin skin test (TST) positivity among contacts was analyzed using hierarchical logistic regression, TST positivity was weakly associated with diabetic status of source case (aOR 1.4, 95% CI 1.0-1.9). Contacts of diabetic TB patients were also more likely to receive isoniazid treatment for latent TB infection (OR 1.8, 95%CI 1.2-2.7). The secondary objective of the study was to determine whether the combination of active TB and diabetes was associated with increased health care costs, as compared to active TB without diabetes. Univariate and multivariate linear regression analyses were performed to estimate the effect of diabetes on duration of hospitalization and TB therapy. Diabetes was not a significant predictor of hospitalization duration (adj. coefficient -0.7d, 95% CI -12.3d to11.0d), while concomitant kidney disease was the strongest predictor (adj. coefficient +14.3d, 95% CI 1.0d to 27.6d). Neither diabetes nor kidney disease was associated with longer TB therapy. However, diabetes and kidney disease each increased the risk of treatment failure, and were the strongest predictor of treatment failure when combined (aOR 13.6, 95%CI 1.6-119.4). This study did not identify an association of diabetes with tuberculosis transmission as detected by linked secondary active cases, although index patients with active TB had a relatively higher proportion of contacts with positive tuberculin tests. Diabetes and renal disease were both associated with an increased risk of TB treatment failure.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| 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".