Standardised incidence ratios (SIRs) for cancer after renal transplant in systemic lupus erythematosus (SLE) and non-SLE recipients
Bibliographic record
Abstract
OBJECTIVE: We investigated malignancy risk after renal transplantation in patients with and without systemic lupus erythematosus (SLE). METHODS: Using the United States Renal Data System from 2001 to 2009, 143 652 renal transplant recipients with and without SLE contributed 585 420 patient-years of follow-up to determine incident cancers using Medicare claims codes. We calculated standardised incidence ratios (SIRs) of cancer by group using age, sex, race/ethnicity-specific and calendar year-specific cancer rates compared with the US population. RESULTS: 10 160 cancers occurred at least 3 months after renal transplant. Overall cancer risk was increased in both SLE and non-SLE groups compared with the US general population, SIR 3.5 (95% CI 2.1 to 5.7) and SIR 3.7 (95% CI 2.4 to 5.7), respectively. Lip/oropharyngeal, Kaposi, neuroendocrine, thyroid, renal, cervical, lymphoma, liver, colorectal and breast cancers were increased in both groups, whereas only melanoma was increased in SLE and lung cancer was increased in non-SLE. In Cox regression analysis, SLE status (HR 1.1, 95% CI 0.9 to 1.3) was not associated with increased risk of developing cancer, adjusted for other independent risk factors for developing cancer in renal transplant recipients. We found that smoking (HR 2.2, 95% CI 1.2 to 4.0), cytomegalovirus positivity at time of transplant (HR 1.3, 95% CI 1.2 to 1.4), white race (HR 1.2, 95% CI 1.2 to 1.3) and older recipient age at time of transplantation (HR 1.0 95% CI 1.0 to 1.2) were associated with an increased risk for development of cancer, whereas shorter time on dialysis, Epstein-Barr virus or HIV were associated with a lower risk for development of cancer. CONCLUSIONS: Cancer risk in renal transplant recipients appeared similar in SLE and non-SLE subjects, aside from melanoma. Renal transplant recipients may need targeted counselling regarding surveillance and modifiable risk factors.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".