Canadian Anatomic Kidney Score: Quantitative Macroscopic Assessment of Donor Kidney Quality for Transplantation
Bibliographic record
Abstract
Background. The Canadian Anatomic Kidney Score (CAKS) is a novel 6-point grading system that standardizes the gross description of a donor kidney across 3 components—vessels, anatomy, and sticky fat. We hypothesized that the CAKS predicts allograft functional outcomes and provides additional information to the Kidney Donor Profile Index (KDPI) and histologic assessment of the donor kidney. Methods. Single-center cohort of 145 patients who underwent renal transplantation with CAKS analysis between 2018 and 2021. CAKS was prospectively determined before transplantation. Preimplantation core biopsies were assessed according to the Remuzzi score (RS). The primary outcome was 1-y allograft function represented by an estimated glomerular filtration rate (eGFR). Results. Linear regression without adjustment for KDPI or RS showed a significant association between the CAKS and 1-y eGFR (−8.7 mL/min/1.73 m2 per point increase in CAKS; 95% CI, −13.0 to −4.4; P < 0.001). Most of that association was attributed to the vessel component (−12.1; −19.4 to −4.8; P = 0.002). Adjustment for KDPI and RS attenuated the relationship between 1-y function and CAKS (−4.6; −9.5 to 0.3; P = 0.065) and vessel component (−7.4; −15.2 to 0.5; P = 0.068). Conclusions. Anatomic assessment of donor kidneys at the time of transplantation associates with allograft function at 1 y. Vascular assessment appears to make the dominant contribution.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".