Treatment of osteoporosis in an older home care population
Bibliographic record
Abstract
BACKGROUND: Previous research indicates that many patients with fractures indicative of underlying osteoporosis are not receiving appropriate diagnostic follow-up and therapy. We assessed osteoporosis treatment coverage in older home care clients with a diagnosis of osteoporosis and/or prevalent fracture. METHODS: Subjects included 330 home care clients, aged 65+, participating in a longitudinal study of medication adherence and health-related outcomes. Data on clients' demographic, health and functional status and service utilization patterns were collected using the Minimum Data Set for Home Care (MDS-HC). A medication review included prescribed and over-the-counter medications taken in the past 7 days. Criteria for indications for osteoporosis therapy included diagnosis of osteoporosis or a recent fracture. Coverage for treatment was examined for anti-osteoporotic therapies approved for use in 2000. RESULTS: Of the 330 home care clients, 78 (24%) had a diagnosis of osteoporosis (n = 47) and/or had sustained a recent fracture (n = 34). Drug data were available for 77/78 subjects. Among the subjects with osteoporosis or a recent fracture, 45.5% were receiving treatment for osteoporosis; 14% were receiving only calcium and vitamin D, and an additional 31% were receiving drug therapy (bisphosphonate or hormone replacement therapy). The remaining 54.5% of subjects were not receiving any approved osteoporosis therapy. CONCLUSIONS: The high prevalence of undertreatment among a population of older adults with relatively high access to health care services raises concern regarding the adequacy of diagnosis and treatment of osteoporosis in the community.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".