Latent tuberculosis diagnostic tests to predict longitudinal tuberculosis during dialysis: a meta-analysis
Bibliographic record
Abstract
SETTING: Tuberculosis (TB) rates in dialysis patients are more than 10 times greater than in the general population. Recent recommendations advise the use of interferon-gamma release assays (IGRAs) over the tuberculin skin test (TST) to aid in the diagnosis of latent tuberculous infection (LTBI); however, their longitudinal predictive ability for TB development has not been assessed. OBJECTIVE: To determine whether the TST or IGRA are able to predict longitudinal TB development in dialysis patients. DESIGN: We performed a systematic review to determine the longitudinal risk of TB in dialysis patients. Random-effects meta-analysis was used to determine the incidence rate ratio (IRR) of longitudinal TB development and the predictive value of such tests. RESULTS: Eight studies were included. An IRR of 2.59 (95%CI 1.20-5.57) for longitudinal TB was seen in patients with a TST ⩾ 10 mm compared to patients with a TST < 10 mm. The positive predictive value (PPV) of a TST ⩾ 10 mm was 11.93% and the negative predictive value was 94.03%. We were unable to analyse the studies that used IGRAs, as only one study had TB events. CONCLUSION: A TST with a 10 mm cut-off point appears to offer the capability to distinguish long-term risk of TB, with a modest PPV. The predictive value of IGRAs could not be quantified.
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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 | 0.014 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.047 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".