Prophylaxis for Latent Tuberculosis Infection Prior to Anti–Tumor Necrosis Factor Therapy in Low‐Risk Elderly Patients With Rheumatoid Arthritis: A Decision Analysis
Bibliographic record
Abstract
OBJECTIVE: To determine if low-risk elderly patients with rheumatoid arthritis (RA) who screen positive for latent tuberculosis (TB) infection prior to anti–tumor necrosis factor (anti-TNF) therapy should be given isoniazid (INH). METHODS: A Markov model was developed. The base case was a patient age 65 years with RA starting anti-TNF therapy with a positive tuberculin skin test (TST) finding of 5–9 mm, who was born in a country with low TB prevalence and had no other TB risk factors. The decision was 9 months of INH or not. The primary outcome was quality-adjusted life expectancy. Multiple sensitivity analyses were performed. RESULTS: No prophylaxis was favored, with a gain of 1.1 quality-adjusted life days, but the decision was sensitive to several variables. Prophylaxis was favored for patients ages <61 years, if the relative risk (RR) of TB reactivation with RA alone was >2.5, if the RR with anti-TNF therapy was >5.8, or if the utility associated with INH therapy was >0.98. Prophylaxis was also preferred for patients with a TST result >10 mm and for patients from higher risk countries. If 6 months of INH or 4 months of rifampin were used, prophylaxis was preferred, providing that therapy reduced the risk of TB reactivation by >47% and >27%, respectively. CONCLUSION: Withholding prophylaxis prior to anti-TNF therapy may be reasonable for low-risk elderly RA patients with a TST finding of 5–9 mm, although the decision is sensitive to patient preferences. For patients age <61 years from a higher risk country, or with a TST finding >10 mm, prophylaxis is preferred.
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".