Acute ankle sprain in athletes: Clinical aspects and algorithmic approach
Bibliographic record
Abstract
Acute ankle sprain is the most common lower limb injury in athletes and accounts for 16%-40% of all sports-related injuries. It is especially common in basketball, American football, and soccer. The majority of sprains affect the lateral ligaments, particularly the anterior talofibular ligament. Despite its high prevalence, a high proportion of patients experience persistent residual symptoms and injury recurrence. A detailed history and proper physical examination are diagnostic cornerstones. Imaging is not indicated for the majority of ankle sprain cases and should be requested according to the Ottawa ankle rules. Several interventions have been recommended in the management of acute ankle sprains including rest, ice, compression, and elevation, analgesic and anti-inflammatory medications, bracing and immobilization, early weight-bearing and walking aids, foot orthoses, manual therapy, exercise therapy, electrophysical modalities and surgery (only in selected refractory cases). Among these interventions, exercise and bracing have been recommended with a higher level of evidence and should be incorporated in the rehabilitation process. An exercise program should be comprehensive and progressive including the range of motion, stretching, strengthening, neuromuscular, proprioceptive, and sport-specific exercises. Decision-making regarding return to the sport in athletes may be challenging and a sports physician should determine this based on the self-reported variables, manual tests for stability, and functional performance testing. There are some common myths and mistakes in the management of ankle sprains, which all clinicians should be aware of and avoid. These include excessive imaging, unwarranted non-weight-bearing, unjustified immobilization, delay in functional movements, and inadequate rehabilitation. The application of an evidence-based algorithmic approach considering the individual characteristics is helpful and should be recommended.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".