Surgery versus collagen to treat female stress urinary incontinence : physician beliefs and requirements for treatment & a modeled cost-effectiveness analysis
Bibliographic record
Abstract
The Surgery Collagen Incontinence Trial (SCIT) is a randomized controlled trial evaluating the comparative efficacy of surgery versus collagen injection to treat female stress urinary incontinence (SUI). This thesis investigated two issues from SCIT: (1) the trial investigators' use of a consensus estimate that assumed collagen would be preferred as a first line treatment if it was at most 20% less efficacious than surgery; and (2) the cost-effectiveness of surgery and collagen. A physician survey was conducted to help verify the SCIT investigators' consensus estimate. Respondents on average believed surgery was more efficacious than collagen, and they generally had stronger beliefs in the ability of surgery to meet their requirements for remaining the first line treatment for SUI. However, on average, respondents indicated a willingness to use collagen if it was at most approximately 23% less efficacious than surgery. The survey also provided baseline data for future research into how SCIT's results may or may not play a role in changing the views of clinicians. The cost-effectiveness analysis was based on a risk-benefit model (decision-tree) that delineated the success rates, side-effects and complication rates of both surgery and collagen. Probabilities from the physician survey and the published literature were used in the model. Collagen was found to be less costly than surgery, but also less efficacious. Until more is known about collagen's long-term durability, the injection material will probably not gain coverage under Canada's provincial health insurance plans.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".