Undifferentiated epithelioid sarcoma presenting as a fever of unknown origin: a case report
Bibliographic record
Abstract
BACKGROUND: Fever of unknown origin is often a diagnostic dilemma for clinicians due to its extremely broad differential. One of the rarer categories of disease causing fever of unknown origin is malignancies; of these, soft tissue sarcoma is one of the least common. Soft tissue sarcomas make up < 1% of all adult malignancies and often do not present with any systemic manifestations or neoplastic fevers. CASE PRESENTATION: A 73-year-old Caucasian woman presented with a 2-week history of fever and profound fatigue. The only other symptom she endorsed was a transient history of left knee pain, initially thought to be unrelated. There was no clear cause on initial examination and routine investigations, but her C-reactive protein was significantly elevated at 207 mg/L. Blood cultures and a urine culture were drawn. She was admitted to hospital for further investigation and placed on empiric antibiotics. Her blood cultures were negative, but she had one further fever in hospital. Computed tomography scans did not yield a cause of her fever. No vegetations were seen on echocardiography. Antibiotics were stopped as she did not seem to have an acute infectious cause of her fever. No new symptoms developed. She felt well enough to proceed with out-patient follow up and was discharged after 8 days in hospital. At 1-month post-discharge: no resolution of symptoms, but she endorsed a recurrence of her left knee pain. Ultrasound and magnetic resonance imaging revealed a 4.5 × 6.8 × 11.6 cm soft tissue mass, identified as a sarcoma on biopsy. She subsequently underwent a distal femur resection. Final staging was pT2bN0M0. She underwent adjuvant radiation therapy, but was found to have developed metastatic disease. CONCLUSION: This case revealed an atypical presentation of a rare soft tissue sarcoma as the cause of the illness. The etiology behind a fever of unknown origin can be difficult to elucidate, making the approach to investigation particularly important. Repeated history-taking and serial physical examinations can be crucial in guiding investigations and ultimately arriving at a diagnosis. In addition, we believe this case highlights the adage that no seemingly innocuous symptom should be left out when working up a condition with such an extensive and complex differential.
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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) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.008 | 0.005 |
| 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".