Intramuscular myxoid lipoma in the proximal forearm presenting as an olecranon mass with superficial radial nerve palsy: a case report
Bibliographic record
Abstract
BACKGROUND: Extremity lipomas may occur in any location, including the proximal forearm. We describe a case of a patient with an intramuscular lipoma presenting as an unusual posterior elbow mass. CASE PRESENTATION: We discuss the case of a 57-year-old Caucasian man who presented with a tender, posterior elbow mass initially diagnosed as chronic olecranon bursitis. A minor sensory disturbance in the distribution of the superficial radial nerve was initially thought to be unrelated, but was likely caused by mass effect from the lipoma. No pre-operative advanced imaging was obtained because the diagnosis was felt to have already been made. At the time of surgery, a fatty mass originating in the volar forearm muscles was found to have breached the dorsal forearm fascia and displaced the olecranon bursa. Tissue diagnosis was made by histopathology as a myxoid lipoma with no aggressive features. Post-operative recovery was uneventful. CONCLUSION: We present a case of an unusual elbow mass presenting with symptoms consistent with chronic olecranon bursitis, a relatively common condition. The only unexplained pre-operative finding was the non-specific finding of a transient superficial radial nerve deficit. We remind clinicians to be cautious when diagnosing soft tissue masses in the extremities when unexplained physical findings are present.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".