Severe neutropenia following infliximab treatment in a child with ulcerative colitis
Bibliographic record
Abstract
To the Editor: Infusion reactions, skin eruptions, and the potential for infectious complications are recognized adverse events associated with tumor necrosis factor alpha (TNF-α) antagonist therapy.1 Blood dyscrasias are infrequently reported in the medical literature. We report a case of profound neutropenia developing in a pediatric patient with ulcerative colitis (UC) treated with a single infliximab infusion. A previously healthy 8-year-old boy presented with a 5-week history of bloody diarrhea, abdominal pain, and weight loss. Past medical history was unremarkable and the family history was negative for inflammatory bowel disease (IBD) and autoimmune disorders. Initial laboratory parameters revealed a normal hemoglobin of 132 g/L, total white cell count of 15.8 × 109/L, neutrophils of 3.45 × 109/L, and platelets 569 × 109/L. The erythrocyte sedimentation rate (ESR) was 11 mm/h and albumin 42 g/L. Investigations were negative for infection. Upper endoscopy was normal, macroscopically and histologically. Colonoscopy demonstrated a moderate to severe pancolitis with a normal terminal ileum. On histology, there was chronic active colitis, with no granulomata. A diagnosis of UC was made. The Pediatric Ulcerative Colitis Activity Index (PUCAI) score2 was 80, indicating severe colitis. He responded to treatment with intravenous corticosteroids. Subsequent attempts to wean oral prednisone were unsuccessful, despite the addition of sulfasalazine (60 mg/kg/day in divided doses). Repeat colonoscopy demonstrated continuous colonic inflammation.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.011 | 0.010 |
| 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".