Mycobacterial Interferon-γ Release Variations During Longterm Treatment with Tumor Necrosis Factor Blockers: Lack of Correlation with Clinical Outcome
Bibliographic record
Abstract
OBJECTIVE: To assess the performance of serial QuantiFeron-TB Gold In-Tube (QFT-GIT) tests in patients with rheumatic diseases during longterm systemic treatment with biologic therapy, evaluating conversions and reversions in relation to the clinical outcome. METHODS: We conducted a prospective study on patients awaiting biologic agents. At baseline, they had chest radiographs, QFT-GIT tests, and tuberculin skin tests (TST); QFT-GIT was repeated at 3, 6, 12, and 18 months after onset of biologic therapy. In patients with no evidence of latent tuberculosis infection (LTBI) at baseline, TST was repeated at 12 months of biologic treatment. RESULTS: Among patients (n = 102; women 65.7%; median age 47 yrs, range 20-82), 14 (13.7%) were considered as having LTBI because of a minimum of 1 abnormal screening test. The agreement between QFT-GIT and TST was 88% (κ = 0.14). During biologic treatment, both patients with (n = 14) and those without (n = 88) evidence of LTBI at baseline showed conversions and reversions in QFT-GIT results at different timepoints. These fluctuations were not paralleled by significant clinical changes. The TST repeated at 12 months in patients with no evidence of LTBI at baseline continued to be negative. The median baseline interferon-γ (IFN-γ) concentration was not significantly different from that observed at each subsequent timepoint. CONCLUSION: Dynamic changes occur with serial IFN-γ release assay testing in patients treated with biologic therapy that do not correlate with clinical outcome. A careful and integrated evaluation of the patient, including clinical information, should guide the treatment decision. This study was underpowered for definite conclusions and further studies are needed to determine the significance of these findings.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".