Ewing's sarcoma in a young man mimicking lateral elbow pain: A case report with 2 years follow‐up
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Lateral elbow pain represents a common musculoskeletal disorder, mostly non-specific and benign. In rare cases, it can be the first symptom of a severe disease such as Ewing's sarcoma (ES). ES is the second most common primary malignant bone tumor in the young population, with a high probability of an unfavourable prognosis. CASE PRESENTATION: This case report presents the history of a young man presenting to the physical therapist with a diagnosis of "epicondylitis" in the right elbow, which was later revealed to be an aggressive ES of the ulna. Findings raising clinical doubts were (a) constant pain even at night and not dependent on load, (b) significant loss of function, (c) patient's young age, and (d) a palpable mass in the elbow. RESULTS: After diagnosis, the patient received medical (chemotherapy, radiotherapy and surgery) and a rehabilitation program. After treatment, the patient improved elbow function, decreased disability and returned to social participation, maintaining positive outcomes at the 2-year follow-up. DISCUSSION: In summary, this case report emphasizes the importance of differential diagnosis and screening for referral of red flags in physical therapy. Moreover, it describes the rehabilitation of a patient with ES, enriching the scientific literature in the field.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| 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".