Factors Associated With Development of Nonunion or Delayed Healing After an Open Long Bone Fracture
Bibliographic record
Abstract
OBJECTIVES: To determine factors associated with developing nonunion or delayed healing after open fracture. DESIGN: Prospective cohort between 2001 and 2009. SETTING: Three level 1 Canadian trauma centers. PARTICIPANTS: Seven hundred thirty-six (791 fractures) subjects were enrolled. Six hundred eighty-nine (94%) subjects (739 fractures) provided adequate outcome data. INTERVENTION: Subjects were followed until fracture(s) healed; phone interviews and chart reviews were conducted 1 year after fracture. Patient, fracture, and injury information, and time to surgery and antibiotics were recorded during hospitalization. MAIN OUTCOME MEASUREMENTS: Nonunion defined as unplanned surgical intervention after definitive wound closure or incomplete radiographic healing at 1 year and delayed healing defined as 2 consecutive clinical assessments showing no radiographic progression or incomplete radiographic healing between 6 months and 1 year. RESULTS: There were 413 (52%) tibia/fibular, 285 (36%) upper extremity, and 93 (13%) femoral fractures. Nonunion developed in 124 (17%) and delayed healing in 63 (8%) fractures. The median time to surgery was not different for fractures that developed nonunion compared with those who did not (P = 0.36). Deep infection [Odd ratio (OR) 12.75; 95% confidence interval (CI) 6.07-26.8], grade 3A fractures (OR 2.49; 95% CI, 1.30-4.78), and smoking (OR 1.73; 95% CI, 1.09-2.76) were significantly associated with developing a nonunion. Delayed healing was also significantly associated with deep infection (OR 4.34; 95% CI, 1.22-15.48) and grade 3B/C fractures (OR 3.69; 95% CI, 1.44-9.44). Multivariate regression found no association between nonunion and time to surgery (P = 0.15) or antibiotics (P = 0.70). CONCLUSIONS: Deep infection and higher Gustilo grade fractures were associated with nonunion and delayed healing. LEVEL OF EVIDENCE: Prognostic Level I. See Instructions for Authors for a complete description of levels of evidence.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 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".