Osteoporosis management in long-term care. Survey of Ontario physicians.
Bibliographic record
Abstract
OBJECTIVE: To survey physicians in Ontario regarding their approach to diagnosis and treatment of osteoporosis among residents of long-term care facilities. DESIGN: Mailed questionnaire covering physician demographics; current clinical practice relating to osteoporosis; and perceived barriers to prevention, diagnosis, and treatment of the disease. SETTING: Long-term care facilities in Ontario. PARTICIPANTS: Medical directors of long-term care facilities. MAIN OUTCOME MEASURES: Demographic variables; physician attitudes; and practices concerning awareness, diagnosis, and treatment of osteoporosis. RESULTS: Respondents returned 275 of 490 questionnaires, for a response rate of 56.1%. Most respondents (92.4%) were family physicians; 28.7% were caring for more than 100 patients in long-term care. Most (85.8%) saw from one to 10 hip fractures yearly in their practices. Although 49.6% of respondents estimated the prevalence of osteoporosis to be 40% to 80% among their long-term care patients, 45.5% said that they did not routinely assess their patients for the disease, and 26.8% do not routinely treat it. Half (50.9%) of physicians would treat patients at high risk based on clinical history; 47.9% if patients had a vertebral compression fracture on plain x-ray examination; 43.8% if patients were highly functional; 42.0% if osteoporosis were confirmed with bone mineral densitometry; and 30.0% if patients had a recent fracture. Perceived barriers to initiating treatment included cost of therapy, patient or family reluctance to accept therapy, and time or cost of diagnosis. CONCLUSION: Although physicians are aware that patients in long-term care facilities are at high risk for osteoporosis and hip fractures, the disease remains underdiagnosed and undertreated.
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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 | 0.001 | 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".