An economic evaluation of surgery versus collagen injection for the treatment of female stress urinary incontinence.
Bibliographic record
Abstract
OBJECTIVE: To use data from a randomized controlled trial and update an earlier economic evaluation of surgery versus collagen injection for the treatment of female stress urinary incontinence (SUI). MATERIALS AND METHODS: A decision tree model was developed using probabilities of success and complications from a randomized controlled trial. Resource use and cost data were taken from the earlier economic evaluation. The primary outcome was treatment success, which was defined as a negative 24 hour PAD test given 1 year post-treatment. The evaluation was conducted from the 'healthcare system' perspective and separate analyses were undertaken for Ontario and Québec. Sensitivity analyses were used to examine uncertainty in probabilities and costs. RESULTS: Surgery was generally more costly and more successful than collagen injection. Incremental cost effectiveness ratios indicated that the healthcare system would incur an additional cost of $121.08 to $341.35 per additional patient that was successfully treated with surgery. Sensitivity analyses showed that surgery would be less costly and more successful than collagen injection if the postoperative length of hospital stay was reduced to 1 day. Surgery might also be more cost effective than collagen injection if the number of injections used to treat patients were to increase beyond two for treatment successes and four for treatment failures. CONCLUSIONS: Collagen injection is an outpatient procedure without risk of significant morbidity or complications. However, this does not readily translate into a clear cost effective advantage relative to surgery. In some cases, surgery may be more cost effective than collagen injection in the treatment of female SUI.
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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 | 0.019 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".