The Preferred Catheter Type After Prolapse Surgery: A Survey Study of Surgeons
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to determine surgeon preference for catheter type in the management of postoperative urinary retention after prolapse surgery, specifically comparing transurethral indwelling catheters (TIC), clean intermittent self-catheterization (CISC), and suprapubic tubes (SPT). METHODS: Electronic surveys were sent to 1182 urogynecologists and urologists through the American Urogynecologic Society and the Canadian Society of Pelvic Medicine. RESULTS: A total of 247 (21%) surveys were completed, where 53% of the respondents ranked TIC as the best catheter option, compared with 42% for CISC and 4% for SPT (P < 0.0001). Most (75%) of the respondents stated they do not offer their patients a choice in catheter selection. Most (43%) of the respondents ranked ease of use for the patient as the most important catheter characteristic. For ease of use for the patient, 71% of the respondents ranked TIC as the best, compared with CISC and SPT. For all other characteristics (pain/discomfort, infection, catheter malfunction, and return of bladder function), CISC was ranked as the best by the majority. CONCLUSIONS: This study showed that surgeons have a significant preference for TIC over CISC and SPT for the management of postoperative urinary retention, and the majority of surgeons do not offer their patients a choice with regard to catheter type.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".