Bone mineral density measurement and osteoporosis treatment after a fragility fracture in older adults: regional variation and determinants of use in Quebec
Bibliographic record
Abstract
BACKGROUND: Osteoporosis (OP) is a skeletal disorder characterized by reduced bone strength and predisposition to increased risk of fracture, with consequent increased risk of morbidity and mortality. It is therefore an important public health problem. International and Canadian associations have issued clinical guidelines for the diagnosis and treatment of OP. In this study, we identified potential predictors of bone mineral density (BMD) testing and OP treatment, which include place of residence. METHODS: Our study was a retrospective population-based cohort study using data from the Quebec Health Insurance Board. The studied population consisted of all individuals 65 years and older for whom a physician claimed a consultation for a low velocity vertebral, hip, wrist, or humerus fracture in 1999 and 2000. Individuals were considered to have undergone BMD testing if there was a claim for such a procedure within two years following a fracture. They were considered to have received an OP treatment if there was at least one claim to Quebec's health insurance plan (RAMQ) for OP treatment within one year following a fracture. We performed descriptive analyses and logistic regressions by gender. Predictors included age, site of fracture, social status, comorbidity index, prior BMD testing, prior OP treatment, long-term glucocorticoid use, and physical distance to BMD device. RESULTS: The cohort, 77% of which was female, consisted of 25,852 individuals with fragility fractures. BMD testing and OP treatment rates were low and gender dependent (BMD: men 4.6%; women 13.1%; OP treatment: men 9.9%; women 29.7%). There was an obvious regional variation, particularly in BMD testing, ranging from 0 to 16%. Logistic regressions demonstrate that individuals living in long term care facilities received less BMD testing. Patients who had suffered from vertebral fractures, or who had received prior OP treatment or BMD testing, regardless of gender, subsequently received more BMD testing and OP treatments. Furthermore, increasing the distance between a patient's residence and BMD facility precluded likelihood of BMD testing. CONCLUSION: BMD testing rate was extremely low but not completely explained by reduced physical access; gender, age, social status, prior BMD testing and OP treatment were all important predictors for future BMD testing and OP treatment.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".