Comparison of Fracture Identification Using Different Definitions in Healthcare Administrative (Claims) Data
Bibliographic record
Abstract
We identified inconsistency in fracture definitions in a prior review of studies that utilized claims data. Here, we aimed to compare fracture rates estimated using thirteen hip and seven radius/ulna fracture definitions. Our primary analysis compared results in a cohort of 120,363 older adults treated with oral bisphosphonates for ≥3 years. The most inclusive definition (hip: inpatient or emergency diagnosis; radius/ulna: inpatient, emergency, or outpatient diagnosis) served as a referent to compare the number and proportion of fractures captured. In sensitivity analyses, we considered a 180-day washout, excluded fractures associated with trauma; and hip only, excluded: (1) subtrochanteric fractures, and (2) hip replacement procedures. Hip fractures varied by definition in number (52-8058) and incidence (0.7-111.8/10,000 person-years). The second most inclusive definition required one inpatient diagnosis and identified 8% fewer hip fractures than the referent. Excluding hip replacements missed 33% of hip fractures relative to the primary analysis. Radius/ulna fractures also ranged in number (1589-6797) and incidence (22.0-94.3/10,000 person-years). Outpatient data were important, when restricted to inpatient or emergency data, only 78% of radius/ulna fractures were identified. Other than hip replacement procedures, sensitivity analyses had minimal impact on fracture identification. Analyses were replicated in a cohort of patients treated with long-term glucocorticoids. This study highlights the importance and impact of coding decisions on fracture outcome definitions. Further research is warranted to inform best practice in fracture outcome identification.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".