Prediction for spontaneous passage of ureteral stones with indwelling ureteral stent: PASS score
Bibliographic record
Abstract
PURPOSE: To develop a predictive model for the spontaneous passage of ureterolithiasis in patients with indwelling ureteral stents. METHODS: In this retrospective cohort study, we reviewed all patients with ureterolithiasis who underwent ureteral stent placement at our institution from 2015 to 2021. Stone Characteristics, including stone location, density, shape, and diameter, were evaluated using computed tomography (CT). Low-density was defined as < 1000 Hounsfield units (HU). Spontaneous stone passage (SSP) was determinded by follow-up CT imaging or ureteroscopy. Multivariable logistic regression with backward selection was applied to identify predicts of SSP and to construct a predictive model. RESULTS: Among 401 patients, 97 (24.2%) experienced SSP after a median follow-up of 26 days (Interquartile Range [IQR] 23-32). Independent predictors for SSP included low stone density < 1000 (Odds Ratio [OR] 7.45, 95% Confident Interval [CI] 2.79-25.94, p = < 0.001), location at the ureterovesical junction (OR 5.28, 95% CI 2.66-10.93, p = < 0.001), mid to distal ureteral location (OR 2.08, 95% CI 1.03-4.31, p = 0.013) and stone diameter ≤ 5 mm (OR 3.42, 95% CI 1.58-7.94, p = < 0.001). Using these predictors, we developed a three-item PASS Score to estimate the probability of SSP. CONCLUSION: Approximately a quarter of ureteral stones passed spontaneously within 4 weeks of stent placement. The PASS score provides a practical tool for clinicians to estimate the likelihood of SSP and guide personalized treatment planning. External validation is required to confirm its clinical utility.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.002 | 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".