Referrals for positive tuberculin tests in new health care workers and students: a retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: Documentation of test results for latent tuberculosis (TB) infection is important for health care workers and students before they begin work. A negative result provides a baseline for comparison with future tests. A positive result affords a potential opportunity for treatment of latent infection when appropriate. We sought to evaluate the yield of the referral process for positive baseline tuberculin tests, among persons beginning health care work or studies. METHODS: Retrospective cohort study. We reviewed the charts of all new health care students and workers referred to the Montreal Chest Institute in 2006 for positive baseline tuberculin skin tests (> or =10 mm). Health care workers and students evaluated for reasons other than positive baseline test results were excluded. RESULTS: 630 health care students and workers were evaluated. 546 (87%) were foreign-born, and 443 (70%) reported previous Bacille Calmette-Guérin (BCG) vaccination. 420 (67%) were discharged after their first evaluation without further treatment. 210 (33%) were recommended treatment for latent TB infection, of whom 165 (79%) began it; of these, 115 (70%) completed adequate treatment with isoniazid or rifampin. Treatment discontinuation or interruption occurred in a third of treated subjects, and most often reflected loss to follow-up, or abdominal discomfort. No worker or student had active TB. CONCLUSIONS: Only a small proportion of health care workers and students with positive baseline tuberculin tests were eligible for, and completed treatment for latent TB infection. We discuss recommendations for improving the referral process, so as to better target workers and students who require specialist evaluation and treatment for latent TB infection. Treatment adherence also needs improvement.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".