Dementia diagnosis and osteoporosis treatment propensity: A population‐based nested case–control study
Bibliographic record
Abstract
AIM: Increasing age and a diagnosis of dementia both dramatically increase the risk of serious osteoporosis-related sequela. We sought to examine the factors associated with osteoporosis treatment, in relation to dementia diagnosis, in older adults with osteoporosis. METHODS: This was a population-based, retrospective, nested, case-control study utilizing administrative healthcare data from British Columbia, Canada. Community-based individuals aged ≥65 years with an osteoporosis diagnosis and continuous enrolment in the provinces' drug plan between 1991 and 2007 were eligible for inclusion. A multivariate logistic regression model was assembled to examine the relationship between dementia diagnosis, age, sex, other comorbidity, residence and osteoporosis medication dispensation. RESULTS: Almost half of the total osteoporosis cohort (n = 39 452) were dispensed an osteoporosis medication during the study period. Individuals with no dementia diagnosis were dispensed a medication significantly more often than those with a diagnosis of dementia (P < 0.001). Those patients with dementia (n = 13 315), who had been dispensed an osteoporosis drug, were more often younger, female, had not sustained a previous fracture, had ≥ 4 comorbid conditions and lived in the most central health region (P < 0.001). A diagnosis of dementia was found to be a significant negative predictor of osteoporosis drug dispensation (adjusted OR 0.55; 95% CI 0.44-0.69). Increasing comorbidity was significantly associated with receiving treatment (adjusted OR 3.30; 95% CI 2.88-3.78). CONCLUSION: Despite the wide availability of osteoporosis medications, our findings suggest that many older adults with a diagnosis of dementia, but not necessarily fewer comorbid conditions, were not receiving treatment.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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 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".