Assessing the Accuracy of Endoscopic Estimates of Lesion Size in Urology Using <i>In Vitro</i> Models of the Urinary Tract
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Despite the frequency at which urologists endoscopically estimate lesion size, their accuracy has not been established. Our objectives were to determine the accuracy of cystoscopic and ureteroscopic estimates of lesion size using in vitro models of the urinary tract and to assess potential impacting factors. METHODS: Eleven staff urologists and 9 urology learners performed cystoscopy on a series of pig bladders containing mock papillary and flat lesions. Each provided three sets of size estimates: two using only the cystoscope to assess intraobserver agreement and the third with the aid of a ureteral catheter as a visual reference. Similar estimates were made with a flexible ureteroscope on papillary lesions within an inorganic upper urinary tract model. Differences in mean estimates and the agreement between repeated estimates were assessed. RESULTS: The level of endoscopic training did not influence the mean error of estimation (MEE) for either cystoscopy or ureteroscopy regardless of lesion size and appearance. Staff and learners consistently underestimated lesion size with median errors of 34% and 43%, with excellent (median intraclass correlation coefficient [ICC] of 0.97) and fair (median ICC of 0.56) reproducibility for cystoscopy and ureteroscopy, respectively. Use of the visual reference during cystoscopy did not improve the MEE. CONCLUSIONS: Urologists, regardless of their level of training, substantially underestimate lesion size by 34% to 43%. These findings are independent of lesion size and appearance, and the use of a visual reference during cystoscopy. Recognizing this tendency and adjusting estimates accordingly or improving instrumentation should improve clinical and operative 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) | 0.000 | 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".