General Considerations About Foot and Ankle Arthrodesis. Any Way to Improve Our Results?
Bibliographic record
Abstract
Nonunion and adjacent joint osteoarthritis (OA) are known complications after a fusion procedure, and foot and ankle surgeons are commonly exposed to such disabling complications. Determining who is at risk of developing nonunion is essential to reducing nonunion rates and improving patient outcomes. Several evidenced-based modifiable risk factors related to adverse outcomes after foot and ankle arthrodesis have been identified. Patient-related risk factors that can be improved before surgery include smoking cessation, good diabetic control (HbAc1 <7%) and vitamin D supplementation. Intraoperatively, using less invasive techniques, avoiding joint preparation with power tools, using bone grafts or orthobiologics in more complex cases, high-risk patients, nonunion revision surgeries, and filling in bone voids at the arthrodesis site should be considered. Postoperatively, pain management with NSAIDs should be limited to a short period (<2 weeks) and avoided in high-risk patients. Furthermore, early postoperative weight-bearing has shown to be beneficial, and it does not seem to increase postoperative complications. The incidence of surrounding joint OA after foot and ankle fusion seems to increase progressively with time. Owing to its progression and high probability of being symptomatic, patients must be informed consequently, as they may require additional joint fusions, resulting in further loss of ankle/foot motion. In patients with symptomatic adjacent joint OA and unsatisfactory results after an ankle arthrodesis, conversion to total ankle arthroplasty (TAA) has become a potential option in managing these complex and challenging situations.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.017 | 0.014 |
| Insufficient payload (model declined to judge) | 0.031 | 0.015 |
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".