Cardiovascular Disease and Hypertension Risk in Living Kidney Donors: An Analysis of Health Administrative Data in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: Knowledge of any harm associated with living kidney donation guides informed consent and living donor follow-up. Risk estimates in the literature are variable, and most studies did not use a healthy control group to assess outcomes attributable to donation. METHODS: We observed a retrospective cohort using health administrative data for donations which occurred in Ontario, Canada between the years 1993 and 2005. There were a total of 1278 living donors and 6359 healthy adults who acted as a control group. Individuals were followed for a mean of 6.2 years (range, 1-13 years) after donation. The primary outcome was a composite of time to death or first cardiovascular event (myocardial infarction, stroke, angioplasty, and bypass surgery). The secondary outcome was time to a diagnosis of hypertension. RESULTS: There was no significant difference in death or cardiovascular events between donors and controls (1.3% vs. 1.7%; hazard ratio 0.7, 95% confidence interval 0.4-1.2). Donors were more frequently diagnosed with hypertension than controls (16.3% vs. 11.9%, hazard ratio 1.4, 95% confidence interval 1.2-1.7) but were also seen more often by their primary care physicians (median [interquartile range] 3.6 [1.9-6.1] vs. 2.6 [1.4-4.3] visits per person year, P<0.001). CONCLUSIONS: Based on administrative data, the risk of cardiovascular disease was unchanged in the first decade after kidney donation. The observed increase in diagnosed hypertension may be due to nephrectomy or more blood pressure measurements received by donors in follow-up and requires prospective study.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".