Ureteral Access Sheath Use and Stenting in Ureteroscopy: Effect on Unplanned Emergency Room Visits and Cost
Bibliographic record
Abstract
PURPOSE: We studied the impact of stented and unstented ureteroscopy on unplanned emergency room (ER) return visits, medical costs, and whether use of a ureteral access sheath precluded uncomplicated ureteroscopy. PATIENT AND METHODS: A series of 161 consecutive patients undergoing ureteroscopy for renal or ureteral stones was evaluated retrospectively. We examined sex, age, stone size, stone location, use of a ureteral access sheath, use of a ureteral stent, unplanned ER visits, unplanned imaging, and interventions. Medical costs were calculated according to British Columbia Medical Services Plan rates. RESULTS: In the 107 stented and 54 unstented patients, the mean stone sizes were 9 and 7 mm, respectively (P = 0.01), and ureteral access sheaths were used in 55% and 35% (P = 0.002). Stent use did not differ by patient age or sex or stone location. The ER return rates were 17% v 22% for the stented and unstented patients, respectively (P = 0.40), with emergency CT scans being performed in 28% v 75% of the returning patients (P = 0.02), hospital readmission in 22% v 58% (P = 0.05), and urgent decompression in 0 v 25% (P = 0.04). Among patients who were not stented, 37% of those treated using ureteral access sheaths v 14% treated without access sheaths returned to the ER (P = 0.04). The median costs were CDN dollars 1212 for stented and CDN dollars1071 for unstented patients (P < 0.0001). CONCLUSIONS: The unplanned ER return rate is similar whether patients are stented or unstented after ureteroscopy. The median cost saving for unstented patients is approximately CDN dollars140. Use of a ureteral access sheath precludes uncomplicated ureteroscopy, and a ureteral stent should be placed in these cases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".