Detecting latent tuberculosis infection during anti-tumor necrosis factor therapy.
Bibliographic record
Abstract
OBJECTIVES: There is little information regarding the reliability of repeat tuberculin skin tests (TSTs) and interferon gamma release assays (IGRAs) in detecting latent tuberculosis infection (LTBI) in people on anti-tumour necrosis factor (TNF) medication. METHODS: We conducted a prospective, observational study of patients referred to the Saskatoon Tuberculosis (TB) Clinic prior to starting anti-TNF medication. A chest x-ray (CXR), 2-step TST and IGRA (QuantiFERON-TB Gold In-Tube Method) were performed at baseline. Those patients with a baseline TST ≥10 mm and/or a positive IGRA were followed with a clinic visit, CXR, TST and IGRA at 3 and 6 months after starting anti-TNF medication. RESULTS: Of 106 potential patients, 91 consented to participate. Twenty-six patients had a positive (≥ 10 mm) TST or IGRA at baseline; twelve started and stayed on anti-TNF medication through the 6-month follow-up and completed both planned follow-up visits. The baseline mean TST measurement for the 12 participants was 13.9 mm (SD 11.4), increasing to a mean of 16.8 mm (SD 9.3) post-booster. At 3 months post-anti-TNF initiation, there was an overall decrease in TST measurement (mean=10.0 mm; SD 9.3; p=0.013), with measurements <5 mm in 3 of the 12 patients. By the 6-month TST, a response recovery was observed with a mean TST measurement of 14.5 mm (SD 7.7), with 11/12 ≥5 mm. The IGRA was unchanged throughout the study period in all patients. The overall agreement between TST and IGRA was poor (kappa coefficient = 0.180, p=0.020). CONCLUSIONS: We demonstrated a transient but significant decrease in TST response in the first six months of anti-TNF therapy.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".