Cancer Screening in Renal Transplant Recipients: Real-World Data
Bibliographic record
Abstract
Background: Multiple international guidelines have endorsed cancer screening in renal transplant patients. This study aimed to describe a series of patients with post-transplant cancer and to report physicians' adherence to cancer screening guidelines. Methods: This is a retrospective study of cancer patients who had a history of renal transplant. Charts of patients who were treated at our institution between 2012 and 2023 were reviewed, patients' clinical data were collected. Results: Thirty-nine patients were identified. The most common types of cancer were lymphoma (n = 9, 23%), squamous cell carcinoma (SCC) of the skin (n = 8, 20.5%), and breast (n = 6, 15.4%). The median age at diagnosis was 56.5 years (range: 16.9 - 70.2), family history of malignancy was depicted in 18 (46.2%) cases. Chart review and patients' questionnaire revealed that increased risk of malignancy was discussed in seven (18%) out of 39 recipients (P < 0.001) at time of transplant, and only three (7.7%, P < 0.001) patients were on post-transplant age-matched cancer screening. Conclusions: The increased risk of malignancy is a serious post-transplant complication. Lymphoma and non-melanoma skin cancer were the most common cancers. Most patients were not offered routine cancer screening; it is important to raise awareness among nephrologists and caregivers regarding the risk of post-transplant malignancy.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".