Modified C index: Novel predictor of postoperative renal functional loss of laparoscopic partial nephrectomy
Bibliographic record
Abstract
INTRODUCTION: We aimed to develop a scoring system to quantify the distance between the renal hilum and renal tumour, termed the modified C index (m-CI), and to predict renal functional loss (RFL) following laparoscopic partial nephrectomy (LPN). METHODS: The m-CI was measured by using computed tomography in 113 patients who underwent LPN between May 2003 and June 2014. The RFL following LPN was calculated by examining the estimated glomerular filtration rate (eGFR) and radioisotope renography one year postoperatively. The Pythagorean theorem was used to calculate the distance from the tumour centre to the renal hilum. The distance was divided by the tumour radius to obtain the m-CI. The correlation between the m-CI and the postoperative RFL were evaluated using Pearson's coefficient values. Multivariate logistic regression models were used to assess the potential predictive factors of RFL following LPN. The correlation between the m-CI and the operative time, ischemia time, and blood loss during LPN were also evaluated by the unpaired t-test. RESULTS: Pearson's coefficient values between the postoperative RFL and the m-CI and C index were 0.294 and 0.173, respectively. In the multivariate analysis, the resected volume (p=0.031) and m-CI (p=0.036) significantly correlated with the postoperative RFL following LPN. The operative time (p<0.001), ischemia time (p=0.028), and blood loss (p=0.047) during LPN was significantly longer and larger, respectively, in the group with shorter m-CI (≤4.5) than in the group with the longer m-CI (>4.5). CONCLUSIONS: The present study demonstrates that the m-CI can predict RFL following LPN, as well as the surgical difficulty.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".