Bibliographic record
Abstract
Background: After donation, living donors may develop one or more risk factors that increase the likelihood of subsequent cardiac and kidney adverse events. Methods: We conducted a cross-sectional, population-based cohort study using linked healthcare databases to study 979 living kidney donors who had donated between 1994 and 2019 in Alberta, Canada. The primary outcome was the presence or history of cardiac and kidney comorbidities, as of March 31, 2020. Cardiac comorbidities included hypertension, diabetes, or major cardiovascular events (myocardial infarction, stroke, or transient ischemic attack). Kidney comorbidities included sustained low eGFR (<45 mL/min/1.73 m2), moderate-severe proteinuria, or kidney stones. Secondary analyses included the presence of other comorbidities, such as cancer, depression, and chronic pain. Results: The median time since donation was 13 years (IQR 7-19). Of the cardiac comorbidities, hypertension was the most common (31%), followed by diabetes (7%), then major cardiovascular events (5%). Both hypertension and diabetes were present in 5% of donors. For the kidney comorbidities, a history of kidney stones was the most common (12%), while low eGFR (5%) and proteinuria (4%) were uncommon. For the other comorbidities, chronic pain (40%) was most common, followed by depression (36%). Overall, approximately three-quarters of donors had at least 1 comorbidity with the proportion of donors with multimorbidity (≥2 comorbidities) rising with increasing age. Conclusions: Comorbidity and multimorbidity in the living kidney donor population rises with increasing age. The results of this study may inform long-term follow-up care of donors by identifying those who may benefit most from periodic health reviews.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".