Evidence map›Paper›PMID 41986765›Full record

ArticleJournal of medical systems2026

Validation of a Large Language Model Enhanced Frailty Index.

Joseph M Plasek, Marie Leeson, John Lundstedt, Natalie C Ernecoff, Julie M Fiskio, Savanna Plombon, Shoshana Streiter, Stephanie Mueller, Stuart Lipsitz, Julia V Loewenthal and 2 more

Abstract readValidation Study
PubMed Publisher
In one paragraph

Article in Journal of medical systems, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

12 authors.

Joseph M Plasek *Division of General Internal Medicine, Brigham and Women's Hospital, 41 Avenue Louis Pasteur 309A, Boston, MA, USA.
Marie Leeson *Division of General Internal Medicine, Brigham and Women's Hospital, 41 Avenue Louis Pasteur 309A, Boston, MA, USA.
John LundstedtDivision of General Internal Medicine, Brigham and Women's Hospital, 41 Avenue Louis Pasteur 309A, Boston, MA, USA.
Natalie C ErnecoffRAND Corporation, Santa Monica, CA, USA.
Julie M FiskioDivision of General Internal Medicine, Brigham and Women's Hospital, 41 Avenue Louis Pasteur 309A, Boston, MA, USA.
Savanna PlombonDivision of General Internal Medicine, Brigham and Women's Hospital, 41 Avenue Louis Pasteur 309A, Boston, MA, USA.
Shoshana StreiterDivision of General Internal Medicine, Brigham and Women's Hospital, 41 Avenue Louis Pasteur 309A, Boston, MA, USA.
Stephanie MuellerDivision of General Internal Medicine, Brigham and Women's Hospital, 41 Avenue Louis Pasteur 309A, Boston, MA, USA.
Stuart LipsitzDivision of General Internal Medicine, Brigham and Women's Hospital, 41 Avenue Louis Pasteur 309A, Boston, MA, USA.
Julia V LoewenthalDivision of Palliative Care and Geriatric Medicine, Brigham and Women's Hospital, Boston, MA, USA.
Li ZhouDivision of General Internal Medicine, Brigham and Women's Hospital, 41 Avenue Louis Pasteur 309A, Boston, MA, USA.
Anuj K DalalDivision of General Internal Medicine, Brigham and Women's Hospital, 41 Avenue Louis Pasteur 309A, Boston, MA, USA. adalal1@bwh.harvard.edu.

Funding

Demonstrating the potential for electronic health record interoperability to improve patient safety research of older adults over the acute episode of care.R01AG083035 · NIA · BRIGHAM AND WOMEN'S HOSPITAL · PI DALAL, ANUJ K, RUDIN, ROBERT SAMUEL · 2023 to 2025
$2.7M
NIA NIH HHS R01 AG083035
6 · The paper itself

Abstract

backgroundEfficient, validated frailty screening tools are needed to identify high-risk older adults. The electronic frailty index (eFI), derived from diagnostic codes, may under-capture functional deficits, such as those documented in physical therapy (PT) notes.

objectiveThis study evaluated the performance of a standard eFI and an enhanced eFI— augmented with deficits extracted from PT notes using a large language model (LLM)—against a geriatrician-performed frailty index based on a comprehensive geriatric assessment (FI-CGA).

designRetrospective cross-sectional study.

participantsAdults with a PT visit within 30 days prior to FI-CGA at a large health system in Boston, MA between March 2018 and September 2024. MAIN MEASURES: An LLM (GPT4o, Microsoft Azure) was prompted to extract eFI deficits from PT notes. Descriptive statistics were used to report demographics characteristics and performance measures. Discrimination was assessed using AUROC with Delong’s test, and agreement was evaluated using Pearson correlation coefficients. KEY

resultsAmong 2413 subjects, 1534 (63.6%) were frail (FI-CGA > = 0.25). The AUROCs for the enhanced eFI compared to standard eFI predicting geriatrician assessed frailty (FI-CGA) was 70.1% vs. 61.4% (p < 0.001). Pearson correlations were 0.756 for the eFI vs. enhanced eFI, 0.239 for the eFI vs. FI-CGA, and 0.365 for the enhanced eFI vs. FI-CGA.

conclusionsAn LLM-enhanced eFI incorporating functional deficits from PT notes demonstrated improved discrimination and stronger correlation with FI-CGA compared to the standard eFI. Leveraging unstructured narratives from PT notes may improve identification of vulnerable older adults who benefit from early intervention.

Indexed as

Frail ElderlyFrailtyGeriatric AssessmentLarge Language ModelsAgedAged, 80 and overBostonCross-Sectional StudiesFemaleHumansMaleRetrospective StudiesAgedFrail elderlyFrailtyHealth services for the agedLarge language models

Identifiers

PMID41986765

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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.