P.046 Review of the management of Wernicke encephalopathy in pediatrics
Bibliographic record
Abstract
Background: Wernicke encephalopathy (WE) is a neurological emergency defined by acute encephalopathy, oculomotor dysfunction, and ataxia. Pediatric cases of WE are underdiagnosed despite having a similar incidence to adults. There are no available treatment guidelines for pediatric WE. Prompt treatment with thiamine can prevent devastating consequences. Methods: A rapid review of the literature of the past 20 years with selected relevant older articles was conducted for the research question “How does child and adolescent thiamine therapy management for Wernicke Encephalopathy compare to adult guidelines?” All articles reporting the investigation, management and treatment of Wernicke encephalopathy – both non alcohol related and alcohol-related pediatric cases – were included. Articles not reporting clinical outcomes were excluded. Results: Eleven case studies including one available review article, met the inclusion and exclusion criteria. An algorithm was created for the organization of published reports of the management of WE for children and adolescents. Key considerations were included for the prevention, identification, acute and ongoing management of patients with WE. Conclusions: The recognition of risk factors for thiamine deficiency and symptoms of acute WE should prompt immediate treatment with thiamine – as a routine and safe therapy in the pediatric 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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".