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Learning disabilities are cognitive disorders caused by neurological impairments that affect cognitive functions like language and reading, without indicating overall intellectual or developmental challenges. These disabilities differ from global intellectual or developmental disabilities as they are limited to distinct cognitive functions. Common learning disabilities include dysgraphia, dyslexia, and dyscalculia, each of which impacts unique aspects of learning.
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Leveraging open-source large language models (LLMs) in scoping reviews: a case study on disability and AI

Azadeh Bayani1, Leandre Parfait Epoh Ewane2, Davllyn Santos Oliveira Dos Anjos1

  • 1Centre de recherche en santé publique, Université de Montréal et CIUSSS du Centre-Sud-de-l'Île-de-Montréal, Montréal, Canada; Laboratoire Transformation Numérique en Santé (LabTNS), Québec, Canada.

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Large language models (LLMs) show promise for automating scientific reviews, but human oversight remains crucial for optimal results. Integrating LLMs with human judgment enhances review accuracy and reproducibility.

Keywords:
AutomationDisabilityLarge language modelsScoping review

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Area of Science:

  • Scientific methodology
  • Artificial intelligence in research

Background:

  • Large language models (LLMs) offer potential for automating manual tasks in scientific reviews.
  • Tasks include data extraction, literature screening, summarization, and quality assessment.

Purpose of the Study:

  • Evaluate LLM performance in title/abstract screening and full-text data extraction for scoping reviews.
  • Assess LLM effectiveness, efficiency, and integration with human tasks.

Main Methods:

  • Automated title/abstract screening, full-text screening, and data extraction (9 dimensions).
  • Evaluated four open-source LLMs: Mistral, Vicuna, and Llama 3.2 (1B, 3B parameters).

Main Results:

  • Llama 3.2-3B achieved 66% accuracy in title/abstract screening and 65% in full-text screening.
  • Mistral excelled in data extraction across most dimensions; Llama 3.2-3B was best for objectives and implications.

Conclusions:

  • LLMs have potential but limitations in automating scoping reviews.
  • Full automation is sub-optimal; a hybrid approach balances LLM strengths with human judgment.
  • Controlled integration supports review replication, refinement, and follow-up.