ArticleClinical ophthalmology (Auckland, N.Z.)2026
Large Language Models for Rapid Instrument Prototyping: Design and Structural Optimization of the Dry Eye Disease in Pregnancy Questionnaire (DED-PREG).
Article in Clinical ophthalmology (Auckland, N.Z.), 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
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Gestational Dry Eye Disease (DED) affects up to 50% of expectant mothers, yet current diagnostic tools are generic and fail to capture pregnancy-specific symptom patterns. Developing and validating new instruments in this population is logistically and ethically challenging due to recruitment barriers. This study describes the development and computational prototyping of the Dry Eye Disease in Pregnancy Questionnaire (DED-PREG) using a Generative Artificial Intelligence (GenAI) framework. Methods: We utilized a multi-stage in silico framework involving two independent synthetic cohorts. First, a qualitative focus group cohort was generated to simulate clinical dialogues for content derivation, followed by semantic vectorization for algorithmic item reduction. Subsequently, an independent validation cohort of 500 pregnant personas was instantiated. We evaluated the resulting 20-item instrument for internal consistency, structural validity, and test-retest reliability via a longitudinal simulation engine utilizing temporal context injection to model gestational progression across five distinct timepoints (T1-T5). Results: The DED-PREG mapped to three distinct domains: Ocular Symptoms, Functional Impact, and Lifestyle & Environmental Modulators. The instrument demonstrated satisfactory internal consistency (Cronbach's alpha = 0.89) and excellent temporal stability in a strictly stable subsample (ICC = 0.99). Confirmatory Factor Analysis indicated acceptable model fit for synthetic high-dimensional data (CFI = 0.82; RMSEA = 0.11). Longitudinal analysis confirmed the instrument's responsiveness to gestational change (Global Cohen's d = 0.44), with Linear Mixed Models (LMM) revealing a significant interaction between low socioeconomic status and symptom exacerbation (β=0.053, p < 0.001). Conclusion: This study presents the first pregnancy-specific DED instrument structurally optimized via AI simulation. While human validation remains the gold standard, this computational approach demonstrates that GenAI can serve as a rigorous "stress-test" for instrument design, enabling the rapid prototyping of robust clinical tools prior to in vivo deployment.
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.