Epidemiology of Cancer in Kidney Transplant Recipients
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Kidney transplantation is the ideal treatment modality for patients with end-stage kidney disease, with excellent outcomes post-transplant compared with dialysis. However, kidney transplant recipients are at increased risk of infections and cancer because of the need for immunosuppression. Kidney transplant recipients have approximately two to three times greater risk of developing cancer than the general population, and cancer is a major contributor to morbidity and mortality. Most of the increased risk is driven by viral-mediated cancers such as post-transplant lymphoproliferative disorder, anogenital cancers, and Kaposi sarcoma. Nonmelanoma skin cancer is the most frequent type of cancer in kidney transplant recipients, likely due to an interaction between ultraviolet radiation exposure and decreased immune surveillance. Occurrence of the more common types of solid organ cancers seen in the general population, such as breast, prostate, lung, and colorectal cancers, is not, or is only mildly, increased post-transplant. Clinical care and future research should focus on prevention and on improving outcomes for important immunosuppression-related malignancies, and treatment options for other cancers occurring in the transplant setting.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it