Post‐transplant malignancies in pediatric organ transplant recipients
Bibliographic record
Abstract
The majority of cancer diagnoses in pediatric solid organ transplant recipients (SOTRs) are post-transplantation lymphoproliferative disorders (PTLD) or skin cancers. However, pediatric SOTRs are also at significantly elevated risk for multiple other solid and hematological cancers. The risks of specific cancers vary by transplanted organ, underlying disease, and immunosuppression factors. More than one-quarter of pediatric SOTRs develop cancer within 30 years of transplantation and their risk of solid cancer is 14 times greater than the general population. Pediatric SOTRs are at significantly higher risk of cancer-associated death. Improving patient survival among pediatric SOTRs puts them at risk of adult epithelial cancers associated with environmental carcinogenic exposures. Vaccination against oncogenic viruses and avoidance of excessive immunosuppression may reduce the risk of solid cancers following transplantation. Patient and family education regarding photoprotection is an essential component of skin cancer prevention. There is significant variability in cancer screening recommendations for SOTRs and general population approaches are typically not validated for transplant populations. An individualized approach to cancer screening should be developed based on estimated cancer risk, patient life expectancy, and screening test performance.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".