Intravenous immunoglobulin weaning evaluation with zero-shot large language model classification
Emily Burton1,2,3,4, Shrirajh Satheakeerthy2,4, Rudy Goh1,2,3,4
1Lyell McEwin Hospital, Elizabeth Vale, South Australia, Australia.
Objectives:
The aims of this study were to determine the performance of a large language model (LLM) with a zero-shot strategy for the classification of several factors relevant to the consideration of intravenous immunoglobulin (IVIg) weaning.
Background:
In many cases, IVIg should be weaned to prevent excessive resource utilisation and adverse effects.
Methods And Materials:
A cohort study was conducted examining neurology outpatients receiving regular IVIg in a 2-month period. Prespecified criteria were used to determine how many individuals were suitable for IVIg weaning. A LLM was applied to determine the level of performance with which the model could provide answers to the prespecified criteria.
Results:
In the inclusion period, 14 individuals were identified and four patients fulfilled the criteria for possible IVIg weaning. The total annual cost saving with IVIg weaning was conservatively estimated to be $84702.20 (1433.9 g of IVIg annually). The LLM achieved an overall classification accuracy of 78.6% (11/14) when a rule-based approach was applied to the individual criteria that it extracted from notes.
Conclusion:
Further research is indicated to determine the frequency with which patients suitable for IVIg weaning are identified at other centres and the degree to which LLM may be able to assist with this process.
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