Miliary Tuberculosis Following Negative Latent Tuberculosis Infection Screening Prior to Tumor Necrosis Factor-α Antagonists: Implications for Management?
Bibliographic record
Abstract
To the Editor: Tumor necrosis factor-α (TNF-α) antagonists are effective treatments for inflammatory disorders but are associated with an increased risk of infections including reactivation of latent tuberculosis infection (LTBI). We describe a patient who developed miliary TB as a complication of TNF-α antagonist therapy despite negative pretherapy screening for LTBI. A 42-year-old woman with severe psoriatic arthritis presented with a 3-week history of fevers, night sweats, nonproductive cough, and 4-kg weight loss. She had been receiving treatment with adalimumab for 3 years after failing other disease-modifying antirheumatic drugs. A computerized tomography scan of her chest showed widespread nodules, bilateral lower zone patchy consolidation, and moderate splenomegaly, with small areas of nonenhancement consistent with miliary TB (Figure 1). She underwent bronchoscopy, which was smear- and culture-positive for Mycobacterium tuberculosis . She started isoniazid, rifampicin, pyrazinamide, and ethambutol, with resolution of symptoms over the following 2 weeks. Figure 1. Chest radiograph of the patient shows disseminated nodules throughout both lung fields. This patient was born in Ireland and migrated to Australia when 3 months … Address correspondence to Dr. B.W. Teh; E-mail: ben.w.teh{at}gmail.com.
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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 | 0.003 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.012 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".