Searching for consensus in the approach to patients with chronic lateral ankle instability: ask the expert
Bibliographic record
Abstract
PURPOSE: The purpose of this study is to propose recommendations for the treatment of patients with chronic lateral ankle instability (CAI) based on expert opinions. METHODS: A questionnaire was sent to 32 orthopaedic surgeons with clinical and scientific experience in the treatment of CAI. The questions were related to preoperative imaging, indications and timing of surgery, technical choices, and the influence of patient-related aspects. RESULTS: Thirty of the 32 invited surgeons (94%) responded. Consensus was found on several aspects of treatment. Preoperative MRI was routinely recommended. Surgery was considered in patients with functional ankle instability after 3-6 months of non-surgical treatment. Ligament repair is still the treatment of choice in patients with mechanical instability; however, in patients with generalized laxity or poor ligament quality, lateral ligament reconstruction (with grafting) of both the ATFL and CFL should be considered. CONCLUSIONS: Most surgeons request an MRI during the preoperative planning. There is a trend towards earlier surgical treatment (after failure of non-surgical treatment) in patients with mechanical ligament laxity (compared with functional instability) and in high-level athletes. This study proposes an assessment and a treatment algorithm that may be used as a recommendation in the treatment of patients with CAI. LEVEL OF EVIDENCE: V.
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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 | 0.062 | 0.158 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".