Association of erythropoiesis-stimulating agents and the incidence risk of cancer diagnosis among chronic dialysis patients: a nested case–control study
Bibliographic record
Abstract
BACKGROUND: Erythropoiesis-stimulating agents (ESAs) are the cornerstone of the treatment for anemia in end-stage renal disease (ESRD) patients. Although a correlation has been established between ESAs and increased tumor growth among patients with cancer-related anemia, an association with a higher incidence of cancer among chronic dialysis patients remains relatively unclear. METHODS: We completed a nested case-control study in a cohort of 4574 patients who began chronic dialysis treatment between 1 January 2001 and 31 December 2007 in Quebec, Canada, utilizing dialysis registry and administrative databases exclusively to extract our data. We excluded patients with a prior diagnosis of cancer. Eligible cases were identified by the time of initial cancer diagnosis obtained from either the hospital's discharge or physician billing form. We then randomly selected up to 10 controls for each case. ESA exposure was evaluated between 6 and 9 months prior to the initial cancer diagnosis. The mean weekly exposure was used to categorize ESA usage as either a low dose (<30 µg/week), moderate dose (30-70 µg/week) or high dose (>70 µg/week). We estimated the association between ESAs and the risk of developing cancer using a multivariable conditional logistic regression. RESULTS: We identified 419 cases of cancer and 3895 matched controls during the study period. The use of ESAs was associated with a higher risk of cancer {odds ratio [OR] 1.04 [95% confidence interval (CI) 1.02-1.07]}. Specifically, patients in the high exposure group (>70 µg/week) had an increased risk of developing cancer [OR 1.77 (95% CI 1.18-2.66)] compared with patients in the unexposed group. CONCLUSION: High-dose ESA was associated with an increased incidence risk of new cancer diagnosis among chronic dialysis patients.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".