ArticleNorth American Spine Society journal2026
ChatGPT improves readability in validated spine patient-reported outcome measures.
Article in North American Spine Society journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Spine patient-reported outcome measures (PROMs) frequently exceed recommended health literacy thresholds, limiting accessibility. Large language models (LLMs) such as ChatGPT can simplify medical text, but their effects on validated outcome instruments remain unclear. Methods: A cross-sectional analysis of validated spine PROMs was conducted. Seventy-seven PROMs identified in a prior readability analysis were revised using ChatGPT 4.0 through a standardized prompt instructing simplification to a sixth-grade reading level. Pre and postrevision readability metrics were assessed using Readable.com across multiple grade-level and linguistic indices. Revised PROMs were additionally evaluated for content fidelity using a predefined taxonomy assessing alterations in response scales, recall timeframes, and item meaning. Differences were analyzed using the Exact Sign Test (α = 0.05). Results: Eighteen of nineteen linguistic parameters improved significantly following ChatGPT revision (p < .05). Word count decreased by 18%, sentence complexity declined, and all readability indices improved (p < .001). About 7 of 9 grade-level metrics achieved NIH/AMA sixth-grade readability compliance following revision. However, 59.7% of PROMs contained at least one content-related error. The most common errors included alteration of validated response scales (23%), omission or simplification of recall timeframes (18%), and consolidation of multiple items into single prompts (16%). Conclusions: ChatGPT 4.0 substantially improved the readability of validated spine PROMs but frequently introduced structural modifications affecting validated content. Although LLMs may enhance linguistic accessibility, unsupervised PROM revision risks compromising measurement integrity. Structured implementation strategies incorporating expert review and psychometric validation may be necessary before AI-modified PROMs can be integrated into spine outcomes research.
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.