Standardised incidence ratios (SIRs) for cancer after renal transplant in systemic lupus erythematosus (SLE) and non-SLE recipients
Why this work is in the frame
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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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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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