Bibliographic record
Abstract
e congratulate Breau and colleagues for demonstrating that there was good correlation between renal volume as measured by two techniques: the ellipsoid method and 3D volume measurement using specialized volumetric software with contrast-enhanced CT scans. 1 The authors found that the ellipsoid method underestimated 3D volume (170 cm 3 vs.186 cm 3 , respectively), but that almost all were accurate 30%; they also concluded that measuring renal volume is easy and reliable.They also suggested that 3D volume software is not needed for the estimation of differential renal function.Other authors previously showed that the modified ellipsoid method can be performed quickly with high reproducibility and accuracy.2 They showed that the intraclass correlation coefficient (ICC) was higher (r = 0.95) in this study.However, we believe that the correlation between observers using the ellipsoid method is somewhat inferior to that of the method using 3D analysis software.Previously, we reported an ICC of 0.995 in donor patients, meaning that renal volume measurement by the 3D analysis software is highly reproducible and accurate.3 We believe that the ellipsoid method can only be used to measure split renal function in renal donors.In patients with renal masses or nonfunctional tissues, such as renal cysts, it is impossible to measure normally functioning renal parenchyma using the ellipsoid method.In addition, the correlation between two estimated renal volume and DTPA kidney scintigraphy was not shown in this study by Breau and colleagues. 1 The question thus remains as to which result is more correlated with renal function.With advancements and improvements in software and imaging modalities, renal volume can be easily measured
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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 | 0.016 | 0.080 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.018 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.008 | 0.011 |
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".