ArticleAmerican journal of speech-language pathology2025
Large Language Models' Ability to Assess Main Concepts in Story Retelling: A Proof-of-Concept Comparison of Human Versus Machine Ratings.
Article in American journal of speech-language pathology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- To be or not to be aphasic: use of story retelling as a marker in subclinical aphasia.Frontiers in human neuroscience · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
purposeDespite an abundance of manual, labor-intensive discourse analysis methods, there remains a dearth of clinically convenient, psychometrically robust instruments to measure change in real-world communication in aphasia. The Brief Assessment of Transactional Success (BATS) addresses this gap while developing automated methods for analyzing story retelling discourse. This study investigated automation of main concept (MC) analysis of stories by comparing scores from three large language models (LLMs) to those of human raters.
methodAfter watching/listening to each of the eight short video/audio BATS stimuli and retelling each story, 96 persons with aphasia (PWA;
resultsThe Pearson correlation coefficients indicate strong positive linear relationships between LLM and human MC scores. G-theory reliability coefficients also indicate reliable scoring between LLM and human scoring across the spectrum of participants and conditions.
conclusionsThis promising proof-of-concept study affirms the reliability of three LLMs in evaluating BATS story retell MCs and justifies ongoing investigation into their use. Providing clinicians and clinical researchers with automated tools for analyzing discourse without the need for prohibitively labor-intensive manual scoring could be a paradigm shift, potentially revolutionizing the aphasia intervention landscape.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.