Best Stent Length Predicted by Simple CT Measurement Rather than Patient Height
Bibliographic record
Abstract
INTRODUCTION AND OBJECTIVES: Ureteral stent length is important, as stents that are too long might worsen symptoms and too short are at higher risk of migration. The purpose of this study was to determine if patient or radiologic parameters correlate with directly measured ureteral length and if directly measured ureteral length predicts proper stent positioning. METHODS: During stent placement, ureteral length (ureteropelvic junction to ureterovesical junction distance) was directly measured by endoscopically viewing a ureteral catheter (with 1-cm marking) emanating from the ureteral orifice. A 22, 24, or 26 cm stent was chosen to be closest to the measured ureteral length. For ureters >26 cm, a 26 cm stent was chosen. Ends of an "ideally positioned" stent were fully curled in the renal pelvis and bladder, without crossing the bladder midline. Rates of ideal stent position were compared between patients with matching stent and ureteral lengths and those with stent lengths differing by ≥1 cm (mismatched). The measured ureteral length was correlated with patient height, L1-L5 height, and length measured on CT. RESULTS: Fifty-nine ureters from 57 patients were included. Height was reasonably correlated with L1-L5 height (Spearman correlation coefficient [rho] = 0.79), although both were poorly correlated with directly measured ureteral length (rho = 0.18 for height and 0.32 for lumbar height). Ureteral lengths measured on CT correlated well with direct measurement (rho = 0.63 for axial cuts and rho = 0.64 for coronal cuts). Matched stent length was associated with higher rates of ideal stent position than mismatched (100% vs 70.9%, p = 0.006). CONCLUSIONS: CT measurements, rather than height, correlate well with measured length and could be used to choose the appropriate stent length. Stents matching directly measured ureteral lengths are associated with high rates of ideal stent position.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".