ArticleJACC. Advances2026
From Theory to Practice: GPT-Supported Data Extraction in Observational Studies on Transcatheter Aortic Valve Replacement.
Article in JACC. Advances, 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
backgroundManual record abstraction is the standard method for data collection in observational studies but is labor-intensive, error-prone, and difficult to scale, particularly when information is embedded in unstructured electronic health records. Large language models (LLMs) may streamline this process, yet evidence from real-world clinical research remains limited.
objectivesThis study evaluated the performance of a generative pre-trained transformer (GPT)-based LLM in extracting sociodemographic, procedural, and outcome variables from free-text electronic health records of patients undergoing transcatheter aortic valve replacement.
methodsWe conducted a retrospective analysis of medical and nursing records for all transcatheter aortic valve replacement procedures performed at Ca' Foncello Hospital (Treviso, Italy) between January and June 2024. Manual abstraction by 2 reviewers served as the reference standard. Accuracy, sensitivity, and specificity with 95% CIs were calculated. Agreement for continuous variables was assessed using Bland-Altman analyses.
resultsA total of 108 cases were included. GPT achieved accuracy ranging from 0.657 (valve brand) to 1.00 (gender, Barthel index, procedure timings, and several intraoperative complications). Sensitivity reached 1.00 for rare but clinically important events, including intraoperative neurological complications, whereas specificity exceeded 0.90 for most variables. For vital parameters, Bland-Altman analyses demonstrated minimal bias and narrow limits of agreement.
conclusionsGPT-based data extraction showed high accuracy across a broad range of variables, particularly continuous repeated measurements and rare intraoperative outcomes. Performance was lower for some infrequent postoperative events, reflecting sparse true positives. These findings support the feasibility of integrating LLM-assisted extraction into observational research workflows, with further validation needed in larger or multicenter cohorts.
Indexed as
Identifiers
What Socratic 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 Socratic graph.