Postfracture care for older women: gaps between optimal care and actual care.
Bibliographic record
Abstract
OBJECTIVE: To investigate rates of assessment and treatment of osteoporosis among older women during the year after they have had fractures. DESIGN: Observational, historical, population-based cohort study. SETTING: Manitoba, which maintains a comprehensive population-based repository of health care services provided and has a publicly funded health care system. PARTICIPANTS: Women 50 years old and older who had suffered fractures between 1997 and 2002. These women were chosen from among approximately 175,000 women of this age in Manitoba. METHODS: We examined each woman's annual medical record between April 1, 1997, and March 31, 2002, to find any International Classification of Diseases fracture codes that have been consistently associated with osteoporosis. We looked for postfracture care during the first 12 months after fractures: bone mineral density (BMD) testing or treated with osteoporosis pharmacotherapy. Analysis was stratified by type of fracture: designated type 1 fractures (spine or hip) and type 2 fractures (not spine or hip). MAIN OUTCOME MEASURES: Use of BMD testing or osteoporosis pharmacotherapy during the first 12 months following fractures. RESULTS: For type 1 fractures, BMD assessment during the first year after fracture increased from 2.6% in 1997-1998 to 4.6% in 2001-2002 (P for trend .0004). Rates of therapy with osteoporosis medication increased from 4.9% in 1997-1998 to 17.6% in 2001-2002 (P for trend < .0001). Results were similar for type 2 fractures. In the final year of the study, only 20.5% of women with either type of fracture underwent any identifiable intervention (BMD assessment or osteoporosis pharmacotherapy). The intervention rate was substantially higher among women 50 to 64 years old (26.4%) than among those 75 years old or older (17.9%, P for trend < .0001). CONCLUSION: Women at highest risk of future fractures are assessed infrequently for osteoporosis with BMD testing and given pharmacotherapy to prevent future fractures just as infrequently. This gap in care was particularly striking for BMD testing despite the fact that testing is free in Manitoba's publicly funded system. Data from this study could be educational for physicians treating osteoporosis and should encourage them to improve their practice patterns and optimize patient care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".