Nonunion in Foot and Ankle Arthrodesis Surgery: Review of Risk Factors, Identification of High-risk Patients, and a Guide to Perioperative Testing and Optimization
Bibliographic record
Abstract
Foot and ankle arthrodesis surgery is often associated with high rates of nonunion ranging from 8% to 40%. This complication can result in individual patient burden and system burden in the management of these complex patients. Biologic factors contribute greatly to the development of a nonunion, including patient-related modifiable risk factors, metabolic and endocrine factors, systemic disease, previous surgeries, medications, weight loss treatments, and posttraumatic and postsurgical factors. Despite the high nonunion rate, there is a lack of high-level evidence in the identification of high-risk patients, strategies to minimize nonunion, and the management of patients with nonunion. An accepted standard of practice has not been established. This review aims to provide foot and ankle surgeons with (1) a comprehensive review of risk factors for nonunion, (2) a tool to identify high-risk patients using a preoperative patient questionnaire, (3) a clinical practice guide to preoperative and intraoperative testing that aims to improve preoperative counselling and patient optimization, and (4) perioperative strategies to minimize nonunion risk. With the above framework, our goal is to minimize nonunion risk in patients undergoing foot and ankle arthrodesis surgery to improve patient care and outcomes.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".