Determination of optimal stent length: a survey of urologic surgeons
Bibliographic record
Abstract
Introduction: Ureteral double-J stent length is an important factor affecting stent-related symptoms. Multiple techniques exist to determine ideal stent length for a given patient, however, little is known about what techniques urologists rely on. Our objective was to identify how urologists determine optimal stent length. Material and methods: An online survey was e-mailed in 2019 to all members of the Endourology Society. The survey sought to assess what methods are commonly used to determine choice of stent length, along with frequency of stent placement post ureteroscopy, duration of stenting, availability of different stent lengths and the use of stent tether. Results: 301 urologists (15.1%) responded to our survey. Following ureteroscopy, 84.5% of respondents would stent at least 50% of the time. Following uncomplicated ureteroscopy, most respondents (52.0%) would leave a stent for 2-7 days. Patient height was most commonly ranked first as the method of choice in determining stent length (47.0%), followed by estimation based on experience only (20.6%) and intra-operative direct measurement of ureteric length (19.1%). Most respondents utilized multiple methods in determination of optimal stent length. Most respondents (66.5%) were interested in a simple intra-operative technique utilizing a special ureteral catheter that would help choose the most appropriate stent length. Conclusions: Post-ureteroscopy stent insertion is common and patient height is the most common method of choice used in determining optimal stent length. Most respondents were interested in using a simple, novel ureteral catheter device that would allow them to more accurately select optimal stent length.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".