Cost-effectiveness of alternative treatments for women with osteoporosis in Canada
Bibliographic record
Abstract
BACKGROUND: During the years following menopause, estrogen levels decline leading to accelerated bone loss and an increased risk of osteoporosis and osteoporosis-related fractures. METHODS: Using a Markov model and decision analytic techniques, the long-term costs and outcomes of five treatment and secondary prevention strategies for osteoporosis were compared: 'no intervention', alendronate, etidronate, risedronate, and raloxifene. The base case analysis examined postmenopausal (65 year old) osteoporotic women without prior fracture. Probabilistic sensitivity analysis (PSA) was used to incorporate the impact of parameter uncertainty, and deterministic sensitivity analysis (DSA) was used to compare alternative patient populations and modeling assumptions. Life years and Quality Adjusted Life Years (QALYs) were used as measures of effectiveness. RESULTS: In the base case analysis, risedronate was dominated by etidronate and alendronate. Alendronate and etidronate were projected to have similar costs and QALYs, and the efficiency frontier was represented by 'no intervention', etidronate, alendronate, and raloxifene (Can$32 571, Can$38 623 and Can$114 070 per QALY respectively). Alternative assumptions of raloxifene's impact on CHD and breast cancer, alternative discount rates and alternative patient risk factors (e.g., starting age of therapy, CHD risk, and prior fracture risk) had significant impacts on the overall cost-effectiveness results for both the bisphosphonates and raloxifene. DISCUSSION: Using conventionally quoted benchmarks and compared to no therapy, alendronate, etidronate, and raloxifene would all be considered cost-effective alternatives for treating women with osteoporosis. Potential limitations of this study include the usual caveats and cautions associated with long-term projection models and the fact that not all inputs into the model are Canadian data sources.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".