Canadian cost data associated with treating overactive bladder is lacking
Bibliographic record
Abstract
INTRODUCTION: Cost-effectiveness analysis forms an integral part of the approval process for new medical treatments in Canada, including drug and non-drug technologies. This study's primary objective was to identify peer-reviewed studies that report Canadian-specific cost data for treating overactive bladder (OAB) based on the Canadian Urological Association (CUA) guideline. A secondary objective was to identify studies that report cost data from other healthcare jurisdictions that could be generalizable to the Canadian context. METHODS: We conducted a systematic review of the published peer-reviewed literature. We included studies from Organization for Economic Cooperation and Development countries, excluding the U.S., published in English since January 2009. RESULTS: From 165 abstracts identified in our initial search, 18 studies were ultimately included for analysis. This included one Canadian-based study reporting costs in Canadian dollars, all related to second-line treatments. The other studies were primarily from Europe, reporting costs in Euros or British pounds. There were no studies reporting costs for first-line treatments. Gaps in costs for select second-line and third-line treatments recommended by the CUA were also identified. CONCLUSIONS: Canadian-specific cost data for OAB treatments published in the peer-reviewed literature is limited to a single study reporting costs for only a few second-line treatments sourced from a single province over 10 years ago. Cost data from other healthcare jurisdictions are available, but the generalizability of costs associated with third-line treatments is questionable.
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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 | 0.023 | 0.152 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.016 | 0.038 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".