Utility of Semiquantitative Polymerase Chain Reaction for Epstein‐Barr Virus to Measure Virus Load in Pediatric Organ Transplant Recipients with and without Posttransplant Lymphoproliferative Disease
Bibliographic record
Abstract
We examined the utility of Epstein-Barr virus (EBV) load as a test for the presence of posttransplant lymphoproliferative disease (PTLD). A semiquantitative (SQ) EBV polymerase chain reaction (PCR) on peripheral blood mononuclear cells (PBMC) was used to determine virus load. We compared the values from pediatric patients, both with and without PTLD, with those from healthy pediatric and adult subjects. The virus loads for asymptomatic healthy subjects had a range of 0-1 log10 cells/10(6) PBMCs. Among transplant recipients (n=135), the mean virus load (+/- standard deviation) at the time of diagnosis of PTLD was 3.1+/-1.2 log(10) cells/10(6) PBMCs versus a baseline value of 1.3+/-1.4 log(10) cells/10(6) PBMCs in children without PTLD (P<.0001). A cutoff of > or =3 log10 cells/10(6) peripheral blood leukocytes resulted in the following values for use of virus load as a test for PTLD: sensitivity, 69%; specificity, 76%; positive predictive value, 28%; and negative predictive value, 95%. We conclude that determination of EBV load by use of SQ PCR is more useful in ruling out than in indicating the presence of PTLD.
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 | 0.002 | 0.008 |
| 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 | 0.001 |
| 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".