Diagnostic Accuracy of Blood-Based Tests and Histopathology for Cytomegalovirus Reactivation in Inflammatory Bowel Disease
Bibliographic record
Abstract
BACKGROUND: It is unclear if traditional histopathology and noninvasive blood-based tests are sufficiently accurate to detect cytomegalovirus (CMV) reactivation in inflammatory bowel disease. Therefore, we assessed the diagnostic accuracy of these tests compared with immunohistochemistry (IHC) and tissue polymerase chain reaction (PCR). METHODS: A systematic search of electronic databases was performed from inception through January 2016 for observational studies comparing diagnostic tests for CMV reactivation in inflammatory bowel disease. IHC and tissue PCR were considered reference standards and were used to evaluate the accuracy of blood-based tests and hematoxylin and eosin histopathology. Weighted summary estimates with 95% confidence intervals (CIs) were calculated using bivariate analysis. RESULTS: Nine studies examined the accuracy of blood-based tests for predicting colonic CMV reactivation: 5 studies by pp65 antigenemia and 4 studies by blood PCR. The overall sensitivity was 50.8% (95% CI, 19.9-81.6), the specificity was 99.9% (95% CI, 99-100), and the positive predictive value was 83.8% (95% CI, 58.6-95.0). The sensitivities of pp65 and blood PCR were 39.7% (95% CI, 27.4-52.1) and 60.0% (95% CI, 46.5-73.5), respectively. Nine studies examined the sensitivity of histopathology. The overall sensitivity was 12.5% (95% CI, 3.6-21.4), 34.6% by IHC (95% CI, 13.8-55.4), and 4.7% by tissue PCR (95% CI, 1.2-17.1). CONCLUSIONS: Although blood-based tests seem to predict colonic CMV reactivation, they are insensitive tests. Similarly, histopathology has poor sensitivity for detecting colonic CMV. In agreement with current guidelines, these tests should not replace IHC or tissue PCR for detecting CMV reactivation in inflammatory bowel disease.
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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 | 0.036 | 0.199 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".