Nonunion Risk Assessment in Foot and Ankle Surgery
Bibliographic record
Abstract
BACKGROUND: Nonunion risk factor identification and modification are subjective. We describe and validate a predictive nonunion risk factor model to identify foot and ankle operative patients at risk for nonunion. MATERIALS AND METHODS: One hundred international experts in foot and ankle surgery were surveyed. Nineteen nonunion risk factors were stratified into 3 categories: more significant than, as significant as, and less significant than smoking 1 pack per day. A nonunion risk assessment model was developed by assigning a weighted score to each risk factor, based on its mean score from the survey. A total nonunion risk (TNR) score was calculated for individual patients. It was retrospectively validated in 2 patient cohorts from a single center's prospectively collected end-stage ankle arthritis patient database: 22 cases of ankle and/or hindfoot fusion nonunion and 40 sex- and procedure-matched controls with bony fusion. Analyses included descriptive statistics, logistic regression, and univariate and multivariate linear regression models. RESULTS: The mean TNR score was 6.6 ± 5.6 in controls and 13.5 ± 8.2 in the nonunion group (P < .001). Data showed excellent intraobserver and interobserver correlation coefficients. In a logistic regression model, the risk of nonunion exceeded 9% with a TNR score greater than or equal to 10. Multivariate linear regression analysis, adjusted for age and sex, suggested that lack of fusion site stability and obesity (body mass index greater than 30) were significantly predictive of nonunion. CONCLUSION: The nonunion risk assessment model provides a reliable, sensitive, and specific method for predicting nonunion based on objective patient assessment. Orthopaedic patients at risk for nonunion could benefit from targeted intervention. LEVEL OF EVIDENCE: Level IV, retrospective observational study.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".