Urologist-perceived barriers and perspectives on the underuse of sacral neuromodulation for overactive bladder in Canada
Bibliographic record
Abstract
INTRODUCTION: An estimated 18% of Canadians have overactive bladder (OAB), with approximately 24% of those reporting difficulty adhering to pharmacotherapy. To date, there has been no investigation into barriers facing sacral neuromodulation (SNM) as treatment for OAB in Canada. METHODS: Current Canadian Urological Association members were invited to participate in an anonymous survey. Data collected included open-ended and Likert scale responses addressing barriers to referral for SNM. Qualitative analysis used a Theoretical Domains Framework (TDF), while quantitative responses are reported using descriptive statistics. RESULTS: A response rate of 20.4% (n=142) was obtained. Most respondents believed SNM was underused (n=82, 57.7%) compared to only 6.3% (n=9) who believed it was used adequately. The most commonly cited reasons for not offering SNM were lack of availability (n=85, 59.9%), expertise (n=49, 34.5%), and funding (n=26, 18.3%). Participants were neutral regarding confidence to appropriately recommend SNM to patients (median 3, interquartile range [IQR] 2-4) and were not confident to manage patient care and issues related to SNM devices (median 2, IQR 1-3). On thematic analysis using the TDF, the most prevalent barriers to SNM care were related to infrastructure and resources. A lack of trained experts and lack of knowledge related to SNM use were also commonly identified barriers. CONCLUSIONS: In this first study exploring urologist-perceived barriers to SNM referral for medically refractory OAB in Canada, urologists acknowledge that SNM implantation is underused but did not feel confident in recommending SNM appropriately. A lack of trained experts and poor funding were also identified as major barriers to SNM referral.
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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".