A scorecard for osteoporosis in Canada and seven Canadian provinces
Bibliographic record
Abstract
The scorecard evaluates the burden and management of osteoporosis in Canada and how care pathways differ across Canadian provinces. The results showed there are inequities in patients' access to diagnosis, treatment, and post-fracture care programs in Canada. Interventions are needed to close the osteoporosis treatment gap and minimize these inequities. INTRODUCTION: The purpose of this study was to develop a visual scorecard that assesses the burden of osteoporosis and its management within Canada and seven Canadian provinces. METHODS: We adapted the Scorecard for Osteoporosis in Europe (SCOPE) to score osteoporosis indicators for Canada and seven provinces (British Columbia, Alberta, Saskatchewan, Ontario, Quebec, New Brunswick, and Newfoundland). We obtained data from a comprehensive literature review and interviews with osteoporosis experts. We scored 20 elements across four domains: burden of disease, policy framework, service provision, and service uptake. Each element was scored as red, yellow, or green, indicating high, intermediate, or low risk, respectively. Elements with insufficient data were scored black. RESULTS: Canada performed well on several elements of osteoporosis care, including high uptake of risk assessment algorithms and minimal wait times for hip fracture surgery. However, there were no established fracture registries, and reporting on individuals with high fracture risk who remain untreated was limited. Furthermore, osteoporosis was not an official health priority in most provinces. Government-backed action plans and other osteoporosis initiatives were primarily confined to Ontario and Alberta. Several provinces (Saskatchewan, New Brunswick, Newfoundland) did not have any registered fracture liaison service (FLS) programs. Access to diagnosis and treatment was also inconsistent and reimbursement policies did not align with clinical guidelines. CONCLUSION: Government-backed action plans are needed to address provincial inequities in patients' access to diagnosis, treatment, and FLS programs in Canada. Further characterization of the treatment gap and the establishment of fracture registries are critical next steps in providing high-quality osteoporosis care.
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 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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.019 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".