Paediatric serum sickness-like reaction: A 10-year retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: Serum sickness-like reaction (SSLR) is an acute inflammatory condition affecting predominantly children. The pathophysiology remains unclear, but drugs are considered the main trigger. OBJECTIVE: The aim of this study was to describe the clinical and laboratory features, triggers, and treatment modalities in children diagnosed with SSLR. METHODS: We conducted a 10-year retrospective cohort study including all paediatric patients (0 to 18 years old) with query SSLR referred to the Adverse Drug Reactions Clinic at the Children's Hospital of Western Ontario. Diagnostic criteria included acute skin rash plus joint inflammation with or without fever. RESULTS: We included 83 patients (47 females). Age ranged from 11 months to 12 years (mean 3.2 years). Amoxicillin was the trigger in 82.7% of patients. The mean time between the exposure to the triggering drug and the development of the symptoms was 8.5 days. Urticaria-like and Erythema multiforme-like lesions were present in 35% and 38.5% of the cases, respectively. Joint inflammation affecting hands/feet was present in 60%. Pruritus, lip/eye swelling, and fever were reported in 33, 31, and 45% of patients, respectively. The lymphocyte toxicity assay (LTA) showed incremental T-cell toxicity in 32 of 34 patients. Children that received treatment with antihistamines/nonsteroidal anti-inflammatory drugs (NSAIDs) plus oral steroids had a mean recovery time shorter than those treated only with antihistamines/NSAIDs (6 versus 8 days; P=0.09). CONCLUSIONS: In our study, SSLR was mostly triggered by amoxicillin and had a mean time presentation of 8.5 days. Further prospective and well-conducted studies are needed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".