Health Care Workers and the Initiation of Treatment for Latent Tuberculosis Infection
Bibliographic record
Abstract
BACKGROUND: Despite strong evidence and recommendations supporting the treatment of latent tuberculosis infection, many affected health care workers at risk of acquiring tuberculosis from and potentially transmitting tuberculosis to their patients do not receive treatment. The objective of this study was to determine whether health care workers were less likely than non-health care workers to initiate treatment for latent tuberculosis infection. METHODS: In this retrospective cohort study that used the disease management database from a specialized downtown Toronto tuberculosis clinic, patients with latent tuberculosis infection were included if they had risk factors for progression of disease and were excluded if they had contraindications to treatment. RESULTS: Our final cohort consisted of 308 patients with latent tuberculosis infection. The overall treatment initiation rate was 58%. We found that, when a number of confounding variables, including age, foreign birth, contact with persons with active tuberculosis, tuberculosis skin test conversion, bacille Calmette-Guérin vaccination, abnormal chest radiograph findings, comorbidities, and income, were considered and/or controlled for, the odds of a health care worker initiating treatment were approximately one-half of those of a non-health care worker (adjusted odds ratio, 0.55; 95% confidence interval, 0.32-0.93). CONCLUSION: We conclude that, in our clinic, health care workers are less likely than non-health care workers to initiate treatment for latent tuberculosis infection.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".