Accuracy of Endoscopic Intraoperative Assessment of Urologic Stone Size
Bibliographic record
Abstract
INTRODUCTION: Endoscopic treatment of renal calculi relies on surgeon assessment of residual stone fragment size for either basket removal or for the passage of fragments postoperatively. We therefore sought to determine the accuracy of endoscopic assessment of renal calculi size. MATERIALS AND METHODS: Between January and May 2013, five board-certified endourologists participated in an ex vivo artificial endoscopic simulation. A total of 10 stones (pebbles) were measured (mm) by nonparticipating urologist (N.D.P.) with electronic calibers and placed into separate labeled opaque test tubes to prevent visualization of the stones through the side of the tube. Endourologists were blinded to the actual size of the stones. A flexible digital ureteroscope with a 200-μm core sized laser fiber in the working channel as a size reference was placed through the ureteroscope into the test tube to estimate the stone size (mm). Accuracy was determined by obtaining the correlation coefficient (r) and constructing an Altman-Bland plot. RESULTS: Endourologists tended to overestimate actual stone size by a margin of 0.05 mm. The Pearson correlation coefficient was r=0.924, with a p-value<0.01. The estimation of small stones (<4 mm) had a greater accuracy than large stones (≥4 mm), r=0.911 vs r=0.666. Altman-bland plot analysis suggests that surgeons are able to accurately estimate stone size within a range of -1.8 to +1.9 mm. CONCLUSIONS: This ex vivo simulation study demonstrates that endoscopic assessment is reliable when assessing stone size. On average, there was a slight tendency to overestimate stone size by 0.05 mm. Most endourologists could visually estimate stone size within 2 mm of the actual size. These findings could be generalized to state that endourologists are accurately able to intraoperatively assess residual stone fragment size to guide decision making.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".