Predictors of nonunion and reoperation in patients with fractures of the tibia: an observational study
Bibliographic record
Abstract
BACKGROUND: Tibial shaft fractures are the most common long bone fracture and are prone to complications such as nonunion requiring reoperations to promote fracture healing. We aimed to determine the fracture characteristics associated with tibial fracture nonunion, and their predictive value on the need for reoperation. We further aimed to evaluate the predictive value of a previously-developed prognostic index of three fracture characteristics on nonunion and reoperation rate. METHODS: We conducted an observational study and developed a risk factor list from previous literature and key informants in the field of orthopaedic surgery, as well as via a sample-to-redundancy strategy. We evaluated 22 potential risk factors for the development of tibial fracture nonunion in 200 tibial fractures. We also evaluated the predictive value of a previously-identified prognostic risk index on secondary intervention and/or reoperation rate. Two individuals independently extracted the data from 200 patient electronic medical records. An independent reviewer assessed the initial x-ray, the post-operative x-ray, and all available sequential x-rays. Regression and chi-square analysis was used to evaluate potential associations. RESULTS: In our cohort of patients, 37 (18.5%) had a nonunion and 27 (13.5%) underwent a reoperation. Patients with a nonunion were 97 times (95% CI 25.8-366.5) more likely to have a reoperation. Multivariable logistic regression revealed that fractures with less than 25% cortical continuity were predictive of nonunion (odds ratio = 4.72; p = 0.02). Such fractures also accounted for all of the reoperations identified in our sample. Furthermore, our data provided preliminary validation of a previous risk index predictive of reoperation that includes the presence of a fracture gap post-fixation, open fracture, and transverse fracture type as variables, with an aggregate of fracture gap and an open fracture yielding patients with the highest risk of developing a nonunion. CONCLUSIONS: We identified a significant association between degree of cortical continuity and the development of a nonunion and risk for reoperation in tibial shaft fractures. In addition, our study supports the predictive value of a previous prognostic index, which inform discussion of prognosis following operative management of tibial fractures.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".