Socioeconomic Status and Choice of Primary Management of Stress Urinary Incontinence: A Retrospective Cohort Study
Bibliographic record
Abstract
OBJECTIVES: Stress urinary incontinence (SUI) affects 25% of Canadian women. Characterizing socioeconomic barriers is essential for advocacy and patient-centred counselling. Our objective was to estimate the association between socioeconomic status and the primary management strategy among patients presenting for consultation for management of SUI. METHODS: This retrospective cohort study used billing and diagnostic data from 2019 to 2022 to identify all patients with SUI presenting to a tertiary urogynaecology clinic in Nova Scotia, Canada. Socioeconomic status was estimated using after tax neighbourhood income quintiles derived using postal codes from 2021 Canadian census data and the PCCF+ 8A1 tool. The primary outcome was a composite of interventions not covered by our provincial health care plan. Logistic regression models were used to estimate the association between neighbourhood income and choice of patient cost-incurring interventions. RESULTS: A total of 293 patients had a diagnosis of SUI and met criteria for inclusion: 4.4% chose expectant, 71.7% chose a conservative intervention that incurred patient cost, and 17.1% chose surgery. There was no difference in the odds of choosing a conservative intervention that incurred patient cost between those from low- and high-income neighbourhoods (OR 1.33; 95% CI 0.75-2.34). Fewer patients from low-income neighbourhoods presented for consultation. CONCLUSIONS: There was no difference in the likelihood of choosing a conservative intervention that incurred patient cost among patients with SUI from low- and high-income neighbourhoods. However, fewer patients from low-income neighbourhoods presented for care. Advocacy for equitable access to consultation and treatment may reduce barriers to access.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".