Osteoporosis screening and treatment in Manitoba: a population-based study
Bibliographic record
Abstract
Introduction: Osteoporosis is a bone disease that results in morbidity, mortality, and high healthcare cost. Glucocorticoid (GC) therapy is the most common cause of secondary osteoporosis. The use of aromatase inhibitors (AI) in postmenopausal women results in increases in bone loss of up to 2.5 times. A fracture may be the only clinical manifestation of osteoporosis, hence screening in terms of bone mineral density (BMD) testing to identify those requiring treatment and initiation of osteoporotic treatment when indicated are important steps in the management of the disease. Objective: Assess rates of receipt of BMD tests, and treatment of osteoporosis, and their trends over time in two separate cohorts of high-dose GC users and female breast cancer patients on AI. Method: Administrative healthcare data was used to conduct a retrospective population-based cohort study of individuals ≥ 40 years of age on GC, and AI between 1997 and 2017. BMD test, and treatment rates, trends over time, and prescribing physician specialties were assessed. Results: Both BMD testing and treatment rates were low (4.4% and 9.1%, respectively) in our cohort of high-dose GC users (n = 49,753). Treatment rates remained stable and below 17.0% throughout the 20-year study period between1997 and 2017, in the cohort of AI users (n= 6,726), while BMD test rates increased dramatically from 9.8% at the beginning of the study to 61.8% by the end of the study. For the GC cohort, treatment rates increased from 3.9% at the beginning of the study, to 15.0% in 2003, decreasing steadily thereafter to 6.8% by the end of the study. The majority of the first prescriptions for high-dose GC (74.2%) and AI (53.7%) were written by general practitioners and oncologists, respectively. Conclusion: Although BMD testing rates increased substantially in AI users over the 20-years study span, and FRAX score analysis showed that individuals most at risk had the highest treatment rates in both high-dose GC and AI users, anti-osteoporosis treatment rates appear suboptimal in both cohorts. Efforts to address the increasing osteoporosis management apparent care-gap for these at-risk populations should be considered.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".