Relative survival in patients with cancer and kidney failure
Bibliographic record
Abstract
BACKGROUND: The population with kidney failure is at increased risk of cancer and associated mortality. Relative survival can provide insight into the excess mortality, directly or indirectly, attributed to cancer in the population with kidney failure. METHODS: We estimated relative survival for people all ages receiving dialysis (n = 4089) and kidney transplant recipients (n = 3253) with de novo cancer, and for the general population with cancer in Australia and New Zealand (n = 3 043 166) over the years 1980-2019. The entire general population was the reference group for background mortality, adjusted for sex, age, calendar year and country. We used Poisson regression to quantify excess mortality ratios. RESULTS: Five-year relative survival for all-site cancer was markedly lower than that for the general population for people receiving dialysis [0.25, 95% confidence interval (CI) 0.23-0.26] and kidney transplant recipients (0.55, 95% CI 0.53-0.57). In dialysis, excess mortality was more than double (2.16, 95% CI 2.08-2.25) that of the general population with cancer and for kidney transplant recipients 1.34 times higher (95% CI 1.27-2.41). There was no difference in excess mortality from lung cancer between people with kidney failure and the general population with cancer. Comparatively, there was a significant survival deficit for people with kidney failure, compared with the general population with cancer, for melanoma, breast cancer and prostate cancers. CONCLUSION: Decreased cancer survival in kidney failure may reflect differences in multi-morbidity burden, reduced access to treatment, or greater harm from or reduced efficacy of treatments. Our findings support research aimed at investigating these hypotheses.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".