Mycobacterial Factors Relevant for Transmission of Tuberculosis
Bibliographic record
Abstract
BACKGROUND: Tuberculosis (TB) transmission is associated with patient-related risk factors. However, DNA fingerprint analysis has provided anecdotal evidence suggesting a role for bacteriological factors. METHODS: To examine the importance of the bacteriological component in TB transmission, we investigated the number of tuberculin skin test-positive (TST induration, ≥ 10 mm) contacts and secondary cases observed in contact investigations around TB cases in relation to the size of the genotype cluster the patient belonged to at the time of diagnosis. We also compared the number of TST-positive contacts and secondary cases of patients with drug-resistant and drug-susceptible TB. RESULTS: Larger clusters were independently associated with an increased number of positive contacts. The mean number of positive contacts ranged from 3.8 for clusters of 2 cases, to 4.7 for clusters of 3-10 cases, to 6.0 for cases in clusters of >10 cases (mean increase in number of positive contacts for every extra case in the cluster, 0.21; 95% confidence interval, 0.09-0.26). The mean number of positive contacts was significantly lower among index cases with isoniazid-monoresistant TB (1.6) than among index cases with pan-susceptible TB (4.6; relative number, 0.45; 95% confidence interval, 0.22-0.92). CONCLUSION: These results suggest that spread of tuberculosis also depends on bacteriological factors.
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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 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".