Probability of Rash Related to Gabapentin Therapy in a Child
Bibliographic record
Abstract
OBJECTIVE: To describe a child who developed a skin reaction during gabapentin therapy and discuss how we evaluated the probability of an adverse drug reaction. CASE SUMMARY: An 8-year-old boy with a neurodegenerative disease of unknown origin and an epilepsy disorder developed an urticarial rash and irritability 10 and 4 days, respectively, after the start of gabapentin 20 mg/kg 3 times a day for epilepsy control. Otherwise, the child was well; no changes in other medication or diet had recently been made. After gabapentin discontinuation and administration of one dose of methylprednisolone 10 mg/kg intravenously and diphenhydramine 1 mg/kg every 4 hours via gastric tube, the rash disappeared over 3 weeks. DISCUSSION: In contrast to other antiepileptic drugs, skin reactions to gabapentin are considered uncommon. In adults, reported prevalence of rash possibly related to gabapentin range from 1% to 10%. A postmarketing surveillance study reported gabapentin treatment failure as a consequence of rash in 0.4% of 3000 patients. The product monograph does not mention rash in children. In our patient, assessment using an objective causality scale revealed that the rash was probably caused by gabapentin. CONCLUSIONS: This case, and limited literature data, suggest that gabapentin may cause rash that is severe enough to necessitate discontinuation in a small percentage of children. Further research is needed to determine the actual incidence and severity of gabapentin-related rash in this population.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".