Eagle Syndrome: Case Report, Literature Review, Proposed Classification, and Role of Ultrasound in its Diagnosis and Management
Bibliographic record
Abstract
Eagle syndrome is defined by a spectrum of clinical presentations related to an elongated styloid process or calcified stylohyoid ligament, in the neck, which impinges on the adjacent nerves, arteries, or veins. It is an uncommon finding but one that should be considered in clinical and imaging practice. A variety of symptoms can result. The diagnosis is often delayed, particularly if it is not considered. Although the elongated styloid process may be seen on plain X-ray, contrast-enhanced computerized tomographic angiography (CTA) is considered the diagnostic test of choice. There is, however, a role for ultrasound in both the diagnosis and consideration of the differential diagnosis of Eagle syndrome. Vascular ultrasound specialists should be aware of this entity and be prepared to characterize the findings with ultrasound. Although medical management or endovascular approaches have been described, generally, surgical excision of the styloid process is recommended. Outcomes from such treatment tend to be excellent. Most reports describe a single patient or small series, and the role of duplex scanning has not been discussed. This patient is presented to allow a review of Eagle syndrome, classify the types, and introduce the details of modern high-resolution color duplex scanning in its management.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| 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".