Prevalence, screening and treatment of latent tuberculosis among oral corticosteroid recipients
Bibliographic record
Abstract
To the Editor: Tuberculosis guidelines identify individuals receiving the corticosteroid drug prednisone (or its equivalent) at a dose of >15 mg·day−1 for 2–4 weeks or more as a group at risk of tuberculosis if infected with Mycobacterium tuberculosis [1, 2]. There is an eight-fold increased risk of developing active tuberculosis with such drugs at this dose [3]. However, there is no information on the epidemiology of latent tuberculosis infection (LTBI), screening and treatment among oral corticosteroid users. Tuberculosis guidelines recommend using a threshold of ≥5 mm induration to identify latent infection among oral corticosteroid recipients [1, 2] but this recommendation is not evidence-based. The purpose of this study was to describe the prevalence, screening and treatment of LTBI among oral corticosteroid recipients in the USA. This was a cross-sectional study using US nationally representative, population-level data from the 1999–2000 National Health and Nutrition Examination Survey (NHANES). A description of the survey design and methodology appears elsewhere [4]. Self-reported medication receipt within the past month that required a prescription was collected by NHANES. Medication receipt was confirmed in 83.3% of participants through examiner inspection of prescription containers [5]. Survey participants who received any corticosteroid in an oral formulation within the past month were considered “recipients”. Survey participants who did not receive any oral corticosteroids within the past month were considered “nonrecipients”. Topical, inhaled or intra-articular corticosteroids were not included in this study. Information on total duration of corticosteroid receipt was collected but not dose. A single-step tuberculin skin test (TST) …
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".