Cost Effectiveness of Duloxetine for Osteoarthritis: A Quebec Societal Perspective
Bibliographic record
Abstract
OBJECTIVE: To assess the cost effectiveness of duloxetine compared to other oral postacetaminophen treatments for osteoarthritis (OA) from a Quebec societal perspective. METHODS: A cost-utility analysis was performed enhancing the Markov model from the 2008 OA guidelines of the National Institute for Health and Clinical Excellence (NICE). The NICE model was extended to include opioid and antidepressant comparators, adding titration, discontinuation, and relevant adverse events (AEs). Comparators included duloxetine, celecoxib, diclofenac, naproxen, hydromorphone, and oxycodone extended release (oxycodone). AEs included gastrointestinal and cardiovascular events associated with nonsteroidal antiinflammatory drugs (NSAIDs), as well as fracture, opioid abuse, and constipation, among others. Costs and incremental cost-effectiveness ratios (ICERs) were estimated in 2011 Canadian dollars. The base case modeled a cohort of 55-year-old patients with OA for a 12-month period of treatment, followed by treatment from a basket of post-discontinuation oral therapies until death. Sensitivity analyses (one-way and probabilistic) were conducted. RESULTS: Overall, naproxen was the least expensive treatment, whereas oxycodone was the most expensive. Duloxetine accumulated the highest number of quality-adjusted life years (QALYs), with an ICER of $36,291 per QALY versus celecoxib. Duloxetine was dominant over opioids. In subgroup analyses, ICERs for duloxetine versus celecoxib were $15,619 and $20,463 for patients at high risk of NSAID-related AEs and patients ages >65 years, respectively. CONCLUSION: Duloxetine was cost effective for a cohort of 55-year-old patients with OA, and more so in older patients and those with greater AE risks.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".