Malignancies in a renal transplant population: The St. Michael's Hospital experience
Bibliographic record
Abstract
INTRODUCTION: Previous publications have shown an increased incidence of various malignancies amongst renal transplant populations. The objective of this study was to analyze the rate and types of malignancies occurring in the St. Michael's Hospital renal transplant population and to determine whether our results were comparable to those previously published. METHODS: After approval by the hospital's research ethic board, review of the records and pathology of the 1584 patients in the renal transplant clinic database patients was performed. The reports dated back to the year 1970. RESULTS: Amongst the 1584 renal transplant patients, 106 patients with 132 dysplastic and malignant posttransplant lesions were identified. The highest incidence amid the malignancies was in nonmelanoma skin malignancies squamous cell carcinoma (SCC), basal cell carcinoma, and Kaposi sarcoma, with a total of 32 patients having 54 separate tumors (2.02% of all patients, 43.2% of tumors). Following skin tumors in incidence were genitourinary (28 tumors), gastrointestinal tract (GIT) lesions (8 adenocarcinomas, 14 dysplastic lesions, 1 low grade neuroendocrine tumor/carcinoid), posttransplant lymphoproliferative disorders (PTLDs) (10 cases), gynecologic (6 carcinomas), cervical/anal/vulvar dysplasia and invasive (SCCs) (4), and thyroid (3 papillary tumors). Nine patients had tumors of multiple sites/types. With respect to outcome, 14 patients died of malignancy, with the highest mortality being in the GIT malignancies (six patients). Second in mortality were the PTLD and skin tumor groups. DISCUSSION: Information on the incidence and outcome of various malignancies in renal transplant patients is important in designing guidelines for the follow-up of these patients regarding tumor screening and prevention. The rate of malignancies in our group is comparable to that reported in other centers.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".