CKD Prevalence, Patterns of Treatment, and Outcomes in Patients with Cancer: A Population-Based Cohort Study
Bibliographic record
Abstract
Background: Chronic kidney disease (CKD) may impede optimal cancer treatment and result in worse outcomes. There are limited data to assess receipt of systemic therapy, radiation therapy, and palliative care in patients with cancer and CKD. Methods: We conducted a population-based cohort study of all patients (≥18 years old) with a new cancer diagnosis in Ontario, Canada (2007-2015). We categorized patients according to CKD status at cancer diagnosis [estimated glomerular filtration rate (eGFR) ≥60 (referent group), 45-59, 30-44, 15-29, <15 mL/min/1.73m2, dialysis and transplant recipients]. We used multivariable Fine and Gray proportional hazards models to assess overall survival, receipt of systemic therapy, radiation and palliative care (6-months prior to death) in the 5 most common solid cancers (bladder, breast, colon, prostate, lung) and kidney cancer. Results: We identified 128,489 patients with a new cancer diagnosis, of whom 16% had pre-existing CKD (eGFR <60 mL/min/1.73m2). Patients with the 6 cancers of interest accounted for 73% (93,751). Kidney function at cancer diagnosis was associated with (progressively) worse overall survival in CKD stages 3a-5, dialysis, and transplant recipients (Figure a). Increasing CKD stage was associated with significantly reduced receipt of all treatment modalities [systemic therapy, radiation and palliative care (Figure b-d)]. Patients receiving dialysis had 2-fold increased mortality in bladder, breast and colon cancers, and 3-fold mortality in kidney cancers.Figure -: Adjusted HR by CKD StatusConclusions: In patients with cancer, CKD is associated with reduced exposure to systemic, radiation and palliative treatments and worse overall survival. Strategies to improve cancer care in the CKD population are needed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".