Framework that uses LLMs to generate study triplets and triplet loss to learn embeddings for clustering observational studies prior to meta-analysis, applied to 58 studies on preterm birth cognitive outcomes.
Higgins and Simon G
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Patient identity and clinical features predict brain tumor segmentation accuracy more strongly than model choice, with localized spatial biases consistent across models and no formal fairness guarantees in any.
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Learning study similarity to investigate heterogeneity in meta-analysis using LLMs and triplet loss
Framework that uses LLMs to generate study triplets and triplet loss to learn embeddings for clustering observational studies prior to meta-analysis, applied to 58 studies on preterm birth cognitive outcomes.
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Fairboard: a quantitative framework for equity assessment of healthcare models
Patient identity and clinical features predict brain tumor segmentation accuracy more strongly than model choice, with localized spatial biases consistent across models and no formal fairness guarantees in any.