Variability in the Definition and Perceived Causes of Delayed Unions and Nonunions
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Despite the large number of fracture outcome studies, there remains variability in the definitions of fracture-healing. It is unclear how orthopaedic surgeons are diagnosing and managing delayed unions and nonunions in clinical practice. We aimed to explore the current opinions of orthopaedic surgeons with regard to defining, diagnosing, and treating delayed unions and nonunions in extremity fractures. METHODS: We developed a survey using previous literature, key informants in the field of orthopaedic surgery, and a sample-to-redundancy strategy. Our final survey contained four sections and twenty-nine questions focusing on demographics and surgical experience, definitions of fracture union, prognostic factors for union, and the need for clinical trials. The Internet-based survey and follow-up e-mails were continued until our a priori sample size of a minimum of 320 completed and eligible responses were collected. RESULTS: Three hundred and thirty-five surgeons completed the survey. The typical respondent was a North American, male orthopaedic surgeon or consultant over the age of thirty years who had completed trauma fellowship training, worked in an academic practice, supervised residents, and had more than six years of experience in treating orthopaedic injuries. Most surgeons endorsed a lack of standardization in definitions for delayed unions (73%) and nonunions (55%); almost all agreed that defining a delayed union and nonunion should be done on the basis of both radiographic and clinical criteria (88%). Most respondents believed that the degree of soft-tissue injury (approximately 93%), smoking history (approximately 82%), and vascular disease (approximately 76%) increased the risk of healing complications. CONCLUSIONS: Surgeons use similar prognostic factors to define and assess delayed unions and nonunions, but there is a lack of consensus in the definitions of delayed union and nonunion. The need for standardization and future randomized trials was strongly endorsed.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it