Interferon‐Gamma Release Assays Are a Better Tuberculosis Screening Test for Hemodialysis Patients: A Study and Review of the Literature
Bibliographic record
Abstract
Diagnosing latent tuberculosis (TB) infection (LTBI) in dialysis patients is complicated by poor response to tuberculin skin testing (TST), but the role of interferon-gamma release assays (IGRAs) in the dialysis population remains uncertain. Seventy-nine patients were recruited to compare conventional diagnosis (CD) with the results of two IGRA tests in a dialysis unit. Combining TST, chest x-ray and screening questionnaire results (ie, CD) identified 24 patients as possible LTBI. IGRA testing identified 22 (QuantiFERON Gold IT, Cellestis, USA) and 23 (T-spot.TB, Oxford Immunotec, United Kingdom) LTBI patients. IGRA and CD correlated moderately (κ=0.59). IGRA results correlated with history of TB, TB contact and birth in an endemic country. TST was not helpful in identifying LTBI patients in this population. The tendency for IGRAs to correlate with risk factors for TB, active TB infection and history of TB argues for their superiority over TST in dialysis patients. There was no superiority of one IGRA test over another.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".