Improvements in osteoporosis testing and care are found following the wide scale implementation of the Ontario Fracture Clinic Screening Program
Bibliographic record
Abstract
We evaluated a system-wide impact of a health intervention to improve treatment of osteoporosis after a fragility fracture. The intervention consisted of assigning a screening coordinator to selected fracture clinics to identify, educate, and follow up with fragility fracture patients and inform their physicians of the need to evaluate bone health. Thirty-seven hospitals in the province of Ontario (Canada) were assigned a screening coordinator. Twenty-three similar hospitals were control sites. All hospitals had orthopedic services and handled moderate-to-higher volumes of fracture patients. Administrative health data were used to evaluate the impact of the intervention.Fragility fracture patients (≥50 years; hip, humerus, forearm, spine, or pelvis fracture) were identified from administrative health records. Cases were fractures treated at 1 of the 37 hospitals assigned a coordinator. Controls were the same types of fractures at the control sites. Data were assembled for 20 quarters before and 10 quarters after the implementation (from January 2002 to March 2010). To test for a shift in trends, we employed an interrupted time series analysis-a study design used to evaluate the longitudinal effects of interventions, through regression modelling. The primary outcome measure was bone mineral density (BMD) testing. Osteoporosis medication initiation and persistence rates were secondary outcomes in a subset of patients ≥66 years of age.A total of 147,071 patients were used in the analysis. BMD testing rates increased from 17.0% pre-intervention to 20.9% post-intervention at intervention sites (P < .01) compared with no change at control sites (14.9% and 14.9%, P = .33). Medication initiation improved significantly at intervention sites (21.6-23.97%; P = .02) but not at control sites (17.5-18.5%; P = .27). Persistence with bisphosphonates decreased at all sites, from 59.9% to 56.4% at intervention sites (P = .02) and more so from 62.3% to 54.2% at control sites (P < .01) using 50% proportion of days covered (PDC 50).Significant improvements in BMD testing and treatment initiation were observed after the initiation of a coordinator-based screening program to improve osteoporosis management following fragility fracture.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".