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 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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".