Utility of Cytomegalovirus Quantitative Polymerase Chain Reaction in Tissue Biopsy for the Diagnosis of Cytomegalovirus Gastrointestinal Disease Among Solid Organ Transplant Recipients
Bibliographic record
Abstract
ABSTRACT Background Gastrointestinal (GI) cytomegalovirus (CMV) infection is an important cause of morbidity after solid organ transplantation (SOT), and diagnosis mainly relies on histopathology of GI tissue biopsies. CMV detection by quantitative polymerase chain reaction (qPCR) on tissue biopsy is not routinely performed, but potentially holds many practical advantages. Methods We compared the performance of CMV qPCR on fresh GI biopsies to histopathologic identification for the detection of CMV GI disease. Results Sixty‐one SOT patients with GI symptoms underwent endoscopic assessment, with tissue biopsies obtained. Eleven patients had proven CMV disease by histopathologic detection. Among them, all had a positive qPCR on tissue biopsy (median of 8.7 × 10 7 IU/mL [interquartile range {IQR} 3.1 × 10 7 , 18.2 × 10 7 ]). Of the 49 patients with negative histopathology, 27 (55%) had CMV qPCR‐positive tissue biopsy specimens (median of 43 604 IU/mL [IQR 2923, 497 570]). Receiver operating characteristic analysis for optimal threshold value for CMV qPCR on tissue biopsy for diagnosis of proven CMV GI disease was 147 906 IU/mL (sensitivity 100%, specificity 80%, area under the curve = 0.975). Conclusion Compared to histopathologic detection, CMV qPCR on GI tissue biopsy is highly sensitive for the diagnosis of CMV GI disease in SOT patients, making it a potentially useful adjunctive diagnostic tool for rapid diagnosis in this population.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".