Maximal kidney length predicts need for native nephrectomy in ADPCKD patients undergoing renal transplantation
Bibliographic record
Abstract
INTRODUCTION: Native nephrectomy in patients with autosomal dominant polycystic kidney disease (ADPKD) is performed on a case-by-case basis. We determine if pre-transplant maximal kidney length (MKL) can be used to predict ultimate nephrectomy status. METHODS: We performed a retrospective review of ADPKD patients who underwent renal transplantation at our centre between January 2000 and December 2012. Pre-transplant measurements of MKL alone, MKL adjusted for height (HtMKL), weight (WtMKL) and body mass index (BMI-MKL) were each assessed for their predictive ability via a receiver operating characteristic (ROC) curve analysis. RESULTS: In total, 84 patients met our inclusion criteria, of which 17 (20.2%) underwent native nephrectomy. An MKL ROC curve analysis revealed an area under the curve (AUC) of 0.867 (95% confidence interval [CI] 0.775-0.931; p < 0.001). An optimal cutoff criterion of >21.5 cm revealed a sensitivity of 94.1% (95% CI 71.3-99.9) and specificity of 70.1% (95% CI 57.7-80.7) for eventual nephrectomy. The AUC of HtMKL, WtMKL and BMI-MKL ROC curves did not differ significantly from MKL alone. HtMKL improved specificity, but not overall test performance. The determination of the cut-off MKL may be influenced by the single-centre retrospective nature of this analysis, as well as the fact that renal size was determined by ultrasound and not computerized tomography or magnetic resonance imaging. CONCLUSION: MKL in patients with ADPKD is associated with the eventual need for nephrectomy and may be a useful clinical tool to risk stratify these patients and therefore guide patient conversations to a decision to leave the native kidneys in situ.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".