An atypical case of Whipple’s disease presenting as fever of unknown origin: A brief review
Bibliographic record
Abstract
A 59-year-old woman with epilepsy was admitted to hospital with a 6-year history of fever of unknown origin (FUO). Computed tomography (CT) showed extensive low-attenuation mesenteric and retroperitoneal lymphadenopathy. Investigations for malignancy and infection were negative, including two separate excisional biopsies of lymph nodes. An ascending aortic aneurysm was seen on CT, and a diagnosis of large vessel vasculitis (LVV) was considered. A trial of prednisone for presumed LVV was initiated and then discontinued when positron emission tomography (PET) failed to show vasculitis. Repeat core biopsy of a mesenteric lymph node revealed non-necrotizing granulomatous inflammation and histiocytes with periodic acid–Schiff (PAS)-positive intracellular material. Electron microscopy and polymerase chain reaction (PCR) of the tissue confirmed Tropheryma whipplei. She was treated with ceftriaxone for 2 weeks, followed by long-term combination doxycycline and hydroxychloroquine. The patient's seizure control improved on therapy, raising the suspicion that the seizure disorder was due to Whipple's disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".