Fracture prevalence and treatment with bone-sparing agents: are there urban-rural differences? A population based study in Ontario, Canada.
Bibliographic record
Abstract
OBJECTIVE: To estimate the prevalence of self-reported osteoporotic fractures and use of bone-sparing agents, and to examine if region of residence is associated with fracture or treatment prevalence. METHODS: A census of persons aged > or = 55 years residing in 2 regions of Ontario, Canada (East York, a region within Toronto, and Oxford County), was completed between 1995 and 1998. Region was coded by record linkage of residential postal codes to 1996 Canadian Census data into 4 groups: East York (urban core), and Oxford County subdivided into: urban core, small urban, and rural. Respondents were excluded if they resided outside the regions of interest or were missing fracture data (5%). RESULTS: A total of 26,839 persons (15,541 women) were studied. Nearly 3 times as many women as men reported having had an osteoporotic fracture (14% vs 5%), with 31% and 8%, respectively, taking bone-sparing agents. Controlling for age, a diagnosis of osteoporosis, number of osteoporotic fractures, and height loss, women residing in East York were more likely (OR 1.2, 95% CI 1.0-1.4) to be taking a bone-sparing agent other than estrogen, but less likely to be taking estrogen (OR 0.8, 95% CI 0.7-0.9) compared to those living in rural areas. No regional differences were observed in fracture prevalence, treatment among those with an osteoporotic fracture, or use of a bone-sparing agent among men. CONCLUSION: Further research into regional differences in osteoporosis screening, treatment, and fractures is warranted. This should examine the appropriateness of possible differences, and separate physician practice patterns from patient characteristics, such as willingness to begin treatment with bone-sparing agents.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".