Closing the gap in postfracture care at the population level: a randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Postfracture care is suboptimal, and strategies to address this major gap in care are necessary. We investigated whether notifications sent by mail to physicians and patients would lead to improved postfracture care. METHODS: We conducted a randomized controlled trial (ClinicalTrials.gov identifier NCT00594789) in the province of Manitoba, Canada, from June 2008 to May 2010. Using medical claims data, we identified 4264 men and women age 50 years or older who recently reported major fractures, and who had not undergone recent bone mineral density testing or treatment for osteoporosis. Participants were randomized to three groups: group 1 received usual care (n = 1480), patients in group 2 had mailed notification of the fracture sent to their primary care physicians (n = 1363), and group 3 had notifications sent to both physicians and patients (n = 1421). Bone mineral density testing and the start of pharmacologic treatment for osteoporosis within the following 12 months were documented. RESULTS: Among participants in group 1 (usual care), 15.8% of women and 7.6% of men underwent testing for bone mineral density or started pharmacologic treatment for osteoporosis. Outcome measures improved among participants in group 2 (30.3% of women and 19.0% of men, both p < 0.001) and group 3 (34.0% of women and 19.8% of men, both p < 0.001). No additional benefit was seen with patient notification in addition to physician notification. Combining groups 2 and 3, the absolute increase for the combined end point of bone mineral density testing or pharmacologic treatment was 14.9% (16.4% among women, 11.8% among men). The number needed to notify to change patient care was 7 (6 for women, 6 for men). The adjusted odds ratio (OR) to change patient care in group 2 was 2.45 (95% confidence interval [CI] 2.01-2.98); for group 3 the OR was 2.82 (95% CI 2.33-3.43). INTERPRETATION: This notification system provides a relatively simple way to enhance post-fracture care.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".