Investigating an Incidental Finding on Abdominal CT Scan in a 7‐Year‐Old Child
Bibliographic record
Abstract
A healthy 7-year-old child presented to the emergency room after a handlebar injury. History and physical examination were unremarkable. He was noted to have an elevated erythrocyte sedimentary rate [66 mm/hr (normal, 2–34 mm/hr)], C-reactive protein [45.3 mg/mL (normal, 0.1–1 mg/mL)], mildly elevated white blood cell count [11.07 × 109/L (normal, 4.23–9.99 × 109/L)], urate [346 μmol/L (normal, 111–291 μmol/L)], and microcytosis on red cell morphology. An ultrasound (US) scan revealed a possible splenic hematoma, and a computerized tomography (CT) scan was ordered to further evaluate (Fig. 1). The CT scan showed no trauma-related injury; however, a markedly thickened terminal ileum with aneurysmal dilatation and several adjacent enlarged lymph nodes. These findings raised concerns for possible Crohn disease (CD), or abdominal lymphoma and an urgent colonoscopy was requested. Prior to colonoscopy, this patient's gastrointestinal pathogen multiplex polymerase chain reaction (PCR) panel returned positive for Yersinia enterocolitica, which was then confirmed on serological testing. The plan for colonoscopy was put on hold and the patient had a follow-up US done 2 weeks later which showed terminal ileitis resolution, negating the need for colonoscopy. Isolated terminal ileitis is classically associated with CD, especially in the United States and Canada, which have some of the world's highest prevalence rates (1). However, the differential diagnosis in terminal ileitis is broad and includes infectious, neoplastic, drug-induced, and non-CD autoimmune disorders (2). The gold standard diagnosis of terminal ileitis involves colonoscopy and biopsy (3). This procedure is invasive, has associated risks, and requires bowel preparation. As such, preventing unnecessary colonoscopies is essential. This case highlights the importance of screening for infectious causes prior to colonoscopy, as well as keeping up to date with regional prevalence rates of these pathogens and considering infection in the differential, especially in atypical presentations.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| 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".