A review of North American orthopaedic trauma and fracture registries
Bibliographic record
Abstract
In North America, orthopaedic trauma and fracture registries can be broadly categorized into 4 groups: national registries without subscription, national registries with subscription, for-profit registries, and nontrauma-specific and insurance databases. In the United States, the National Trauma Data Bank is the largest national trauma registry and can be freely accessed but lacks many specifics and outcomes pertinent to patients with fracture as data are only for the inpatient stay. The Orthopaedic Trauma Association/American Academy of Orthopaedic Surgeons Fracture and Trauma Registry is a 2-year-old subscription-based database designed to account for shortcomings in the now defunct Orthopaedic Trauma Association fracture registry. Three additional registries exist with focused patient populations: Own the Bone (a subscription-based database focused on osteoporotic fractures and secondary prevention) and 2 military registries, the Department of Defense Trauma Registry and Military Orthopedic Trauma Registry (both restricted-access databases focusing on combat-related fractures and injuries sustained by military personnel). In Canada, there is no active national trauma registry. The previous National Trauma Registry was operational from 1997 to 2014, but closed because of limited use, reallocation of funding, and lack of data timeliness. Provincial trauma registries are the source of most trauma-specific registry data; however, most fragility fractures and other low-energy injuries are not captured. North America's most widely used registries, the National Trauma Data Bank and Canadian provincial registries, provide little data on fracture-specific outcomes. The addition of fracture-specific variables and outcomes would significantly enhance the ability for these registries to become a more valuable resource for orthopaedic surgeons.
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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 | 0.019 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.028 | 0.052 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".