Exploring beyond viral load testing for EBV lymphoproliferation: Role of serum IL‐6 and IgE assays as adjunctive tests
Bibliographic record
Abstract
Barton M, Wasfy S, Hébert D, Dipchand A, Fecteau A, Grant D, Ng V, Solomon M, Chan M, Read S, Stephens D, Tellier R, Allen UD and the EBV and Associated Viruses Collaborative Research Group. Exploring beyond viral load testing for EBV lymphoproliferation: Role of serum IL‐6 and IgE assays as adjunctive tests. Pediatr Transplantation 2010: 14: 852–858. © 2010 John Wiley & Sons A/S. Abstract: We examined serum IL‐6 and IgE assays as adjuncts to VL monitoring for PTLD. Paediatric solid organ transplant recipients were followed with VL monitoring. VL, IL‐6, and IgE assays were compared between PTLD cases and non‐cases at <3, 3–6 and >6 months after transplantation. Median IL‐6 levels in PTLD cases were 15.5 (2.0–87.1) and 23.3 (2.1–276) pg/mL compared with 3.25 (0.92–114) and 3.5 (0.75–199.25) pg/mL in non‐cases at 3–6 and >6 months, respectively (p = 0.006 and p = 0.005). At >6 months, IL‐6 levels correlated with VL and PTLD occurrence (Spearman’s coefficients = 0.40; p = 0.001 and 0.32; p = 0.003) in univariate analyses. No benefit was derived from performance of IgE levels. The sensitivity and specificity of high VL as a test of PTLD were 76.3% and 92.5%, while the negative predictive value and PPV of VL were 94.9% and 68.4%, respectively. Combining elevated IL‐6 with high VL increased the PPV and specificity to 80% and 96.2%, respectively, and improved the receiver operating characteristic curve. Serum IL‐6 levels can improve the clinician’s ability to identify PTLD, among patients with elevated EBV viral loads.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".