Fracture definitions in observational osteoporosis drug effects studies that leverage healthcare administrative (claims) data: a scoping review
Bibliographic record
Abstract
Healthcare administrative (claims) data are commonly utilized to estimate drug effects. We identified considerable heterogeneity in fracture outcome definitions in a scoping review of 57 studies that estimated osteoporosis drug effects on fracture risk. Better understanding of the impact of different fracture definitions on study results is needed. PURPOSE: Healthcare administrative (claims) data are frequently used to estimate the real-world effects of drugs. Fracture incidence is a common outcome of osteoporosis drug studies. We aimed to describe how fractures are defined in studies that use claims data. METHODS: We searched MEDLINE (Ovid), Embase (Ovid), CINAHL (EBSCO), and gray literature for studies published in English between 2000 and 2020 that estimated fracture effectiveness (hip, humerus, radius/ulna, vertebra) or safety (atypical fracture of the femur, AFF) of osteoporosis drugs using claims data in Canada and the USA. Literature searches, screening and data abstraction were completed independently by two reviewers. RESULTS: We identified 57 eligible studies (52 effectiveness, 3 safety, 2 both). Hip fracture was the most common fracture site studied (93%), followed by humerus (66%), radius/ulna (59%), vertebra (61%), and AFF (9%). Half (n = 29) of the studies did not indicate specific data sources, codes, or cite a validation paper. Of the papers with sufficient detail, heterogeneity in fracture definitions was common. The most common definition within each fracture site was used by less than half of the studies that examined effectiveness (12 definitions in 29 hip fracture papers, 8 definitions in 17 humerus papers, 8 definitions in 13 radius/ulna papers, 9 definitions in 15 vertebra papers), and 3 definitions among 4 AFF papers. CONCLUSION: There is ambiguity and heterogeneity in fracture outcome definitions in studies that leverage claims data. Better transparency in outcome reporting is needed. Future exploration of how fracture definitions impact study results is warranted.
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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 | 0.097 | 0.349 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.013 |
| Bibliometrics | 0.039 | 0.033 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".