Determination of optimal stent length: a survey of urologic surgeons
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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 it