Epidemiology of Post-Transplant Lymphoproliferative Disorders in Solid Organ Transplant Recipients in a Single Canadian Centre.
Bibliographic record
Abstract
Objective: Many epidemiologic studies of post-transplant lymphoproliferative disorder (PTLD) in solid organ transplant (SOT) populations have limited follow-up, incomplete pre-transplant viral serology and PTLD histology, and study only adults or children. We describe a single center study of the PTLD cases in all adult and pediatric (ped) SOT recipients from January 1, 1984 to June 30, 2013. Methods: Patients (n=4750, 475 age < 18, 4275 age ≥ 18) included 724 heart (15.2%), 551 lung or heart/lung (11.6%), 1298 liver (27.3%), 2099 kidney (44.2%), 66 pancreas or kidney/pancreas (1.4%), and 12 multivisceral or small bowel (.3%). Yearly cumulative incidence (CI) was calculated using the patient-year variant of the incidence definition: number of cases/amount of patient-time at risk each year. PTLD risk factors were analyzed using univariate Cox regression analyses, ped and adult patients were analyzed separately. Results: 134 cases of PTLD occurred over a median follow-up (FU) of 5.98 yrs (standard error (SE) 0.09, range 0-29.5 yrs). Ped SOT recipients developed more early lesions and polymorphic PTLD than adults (44 vs. 10%); adults had more monomorphic PTLD (68 vs. 38%). CI of PTLD was 1.31% PY at 1 year, 2.25% PY at 5 years, 3.04% at 10 years, and 3.93% PY at 20 years. Median overall survival (OS) of the entire cohort was 13.8 years (SE .36); PTLD adversely impacted OS in adult (p=.000, HR 1.3 (95% C.Int. 1.1-1.4), but not ped recipients (p=.23). Factors that significantly increased PTLD risk were: younger age at transplant, less time post-transplant, REBV -, and RCMV- (adult only). Risk of early (≤ 1 year) and late (> 1 year) PTLD, early/late EBER+ PTLD, and PTLD localized to CNS, GI, allograft and >1 extranodal (EN) site decreased with increased age at transplant. Risk of early PTLD and allograft PTLD in adults was higher from 1984-1991 compared to 2002-13. Being REBV- conferred significantly higher risk of early and late PTLD, early and late EBER+ PTLD, and PTLD localized in the CNS, GI tract, allograft, and over 1 EN site in adults. Conclusions: PTLD cases continued to arise 20 yrs post-transplant, and risk was increased by younger age, shorter duration post-transplant, and negative EBV serology. Negative EBV serostatus also increased risk of all PTLD subgroups in adults, including CNS involvement.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".