Are renal tumour scoring systems better than clinical judgement at predicting partial nephrectomy complexity?
Bibliographic record
Abstract
INTRODUCTION: We aimed to determine how renal tumour scoring systems, such as RENAL, PADUA, and Centrality (C)-index, compare to clinical judgement at predicting time required for tumour removal and kidney reconstruction during partial nephrectomy. METHODS: 10 to generate a clinical judgement score. Two independent reviewers determined the RENAL, PADUA, and C-index scores. The time to complete tumour resection and renal reconstruction during partial nephrectomy was prospectively recorded. RESULTS: During the study period, 104 partial nephrectomies were performed. The mean partial nephrectomy complexity score based on clinical judgement was 3.4 (standard deviation [SD] 2.1) out of 10. There was good agreement between surgeons in assessing tumour complexity (intraclass correlation coefficient 0.72; 95% confidence interval [CI] 0.65, 0.78). The mean RENAL score was 6.7 (SD 1.6) out of a maximum of 12, the mean PADUA score was 8.5 (SD 1.5) out of a maximum of 14, and the mean C-index score was 3.8 (SD 2). Mean resection and reconstruction time was 24 minutes (SD 10 minutes). The correlation between clinical judgement score and time was 0.27 (p=0.005). The correlation between renal tumour scoring systems and time was 0.20 (p=0.04) for RENAL, 0.21 (p=0.03) for C-index, and 0.26 (p=0.007) for PADUA. RENAL and PADUA scores were significantly associated with surgical and total complications. CONCLUSIONS: The majority of variance in ischemia time is not explained by clinical judgement or renal tumour scoring systems. Renal tumour scoring systems were not better than the clinical judgement of urological oncologists at predicting ischemia time during partial 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 | 0.011 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".