Living kidney donor estimated glomerular filtration rate and recipient graft survival
Bibliographic record
Abstract
BACKGROUND: Kidney transplants from living donors with an estimated glomerular filtration rate (eGFR) < 80 mL/min per 1.73 m(2) may be at risk for increased graft loss compared with a recipient who receives a kidney from a living donor with a higher eGFR. METHODS: This retrospective cohort study considered 2057 living kidney donors and their recipients from July 1993 to March 2010 at five centres in Ontario, Canada, and linked them to population-based, universal healthcare databases. Recipients were divided into five groups based on their donor's baseline eGFR. The median (inter-quartile range) for the lowest eGFR group was 73 (68-77) mL/min per 1.73 m(2). Subjects were followed for a median of 6 years (IQR: 3-10 years). RESULTS: There was no significant difference in the adjusted hazard ratio (HR) for graft loss when comparing recipients in each eGFR category to the referent group (≥110 mL/min per 1.73 m(2)). The adjusted HRs (95% CI) from the lowest (<80 mL/min per 1.73 m(2)) to highest (100-109.9 mL/min per 1.73 m(2)) eGFR categories were 1.27 (0.84-1.92), 1.43 (0.96-2.14), 1.23 (0.86-1.77) and 1.23 (0.85-1.77), respectively. Similar results were observed when dichotomizing the baseline donor eGFR using a cut-point of 80 mL/min per 1.73 m(2)-adjusted HR 1.01 [95% confidence interval (95% CI) (0.76-1.44)]. CONCLUSIONS: Further research in this setting should clarify whether additional tests (i.e. measured GFR) should be performed in potential donors whose eGFR is considered borderline, whether eGFR values should be standardized to body surface area, and the outcomes for donors after nephrectomy.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".