Osteoporosis prescribing trends in primary care: a population-based retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: Osteoporosis is a highly prevalent and costly disease associated with aging. Previous studies have indicated low intervention rates in primary care; however, there is little research investigating the prescribing patterns of osteoporosis medications by primary-care physicians. METHODS: We conducted a population-based retrospective cohort study to examine trends in osteoporosis medication utilization in primary care between 1 January 2000 and 31 December 2009 in Ontario, Canada. All Ontario residents aged 65 years or older and eligible for public health coverage were included in the analysis (∼1.46 million residents in 2000, ∼1.75 million residents in 2009). RESULTS: Analysis of 10-year data indicates a trend toward higher utilization of osteoporosis medications among elderly primary-care patients. In 2000, 100 038 unique patients were prescribed an osteoporosis medication by a family physician; by 2009, this number increased to 301 679. Age-group analyses suggest an inverted U-shaped pattern, whereby utilization rates increase with advancing age and then decline for the oldest age groups. Utilization rates were the lowest for the 100+ age group. CONCLUSIONS: This study indicates increased utilization of osteoporosis-related medications among elderly primary-care patients over a recent 10-year time period. It is unclear whether the observed increase in utilization is due to higher rates of osteoporosis. Further research is needed to determine the appropriateness of this higher utilization.
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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 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".