Diagnostic potential of interferon‐gamma release assay to detect latent tuberculosis infection in kidney transplant recipients
Bibliographic record
Abstract
BACKGROUND: -TB Gold In-Tube test (QFT) for diagnosing LTBI in patients planned for kidney transplantation. METHODS: All adult patients with end-stage renal disease, evaluated for kidney transplantation in a referral center from August 2008 till May 2013, were enrolled, after consenting in a prospective, observational, non-interventional study. LTBI diagnosis was conducted by TST, chest x-ray, and clinical assessment, followed by IGRA by QFT. RESULTS: Overall, 278 patients were enrolled and kidney transplantation was performed in 173 patients. Contributed follow-up was 836.5 patient-years, and TB-free transplant duration was 478.5 patient-years. By standard methods, LTBI was diagnosed in 14 patients. Peri-transplant chemoprophylaxis was given to 53 patients, which included recipients of organs from all deceased donors and living donors with LTBI. QFT was positive in 70 patients, negative in 200 patients, and indeterminate in 8 patients. The agreement between LTBI diagnosis using standard methods and IGRA by QFT was poor (kappa: 0.089+0.046, P-value=.017). Twenty-seven of the QFT-positive patients were transplanted and only one was given isoniazid preventive therapy. None of the transplant recipients developed TB after a median follow-up of 25 months (range 2-58 months, mean 27 months). CONCLUSIONS: The agreement of the QFT with standard diagnosis of LTBI in kidney transplant recipients was poor.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".