Evidence map›Paper›PMID 42064322›Full record

ArticleJAMIA open2026

Large language models for automated and audience-tailored labeling of latent classes.

Fatemeh Gholi Zadeh Kharrat, Rob Werfelmann, Glen Ep Ropella, Wolf Mehling, C Anthony Hunt, Jeffrey Lotz, Thomas A Peterson, REACH Investigators

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PubMed Full text at PMC Publisher
In one paragraph

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

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Fatemeh Gholi Zadeh KharratDepartment of Orthopaedic Surgery, University of California, San Francisco, CA 94143, United States.
Rob WerfelmannTempus Dictum, Inc, Olympia, WA 98502, United States.
Glen Ep RopellaTempus Dictum, Inc, Olympia, WA 98502, United States.
Wolf MehlingDepartment of Family and Community Medicine, Osher Center for Integrative Health, University of California, San Francisco, CA 94115, United States.
C Anthony HuntDepartment of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94143, United States.ORCID https://orcid.org/0000-0001-9372-6860
Jeffrey LotzDepartment of Orthopaedic Surgery, University of California, San Francisco, CA 94143, United States.ORCID https://orcid.org/0000-0002-9654-0647
Thomas A PetersonDepartment of Orthopaedic Surgery, University of California, San Francisco, CA 94143, United States.
REACH Investigators

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: This study compares multiple LLMs, including ChatGPT, DeepSeek, and Llama, to generate meaningful, audience-adapted labels for the existing latent classes among patients with chronic low back pain (cLBP). Methods: Phenotypes were derived from baseline data from two cohorts within the NIH HEAL BACPAC consortium: BACKHOME, a large nationwide e-cohort (train set: Results: Latent Class Model (LCM) identified four distinct behavioral phenotypes in patients with cLBP: Conclusions: These results highlight the possibility of integrating LLM-driven labeling into research and clinical practice, helping to achieve more transparent knowledge translation, improved decision-making, and personalized care.

Indexed as

artificial intelligence (AI)chronic low back pain (cLBP)LangChainLarge language models (LLMs)Latent Class Model (LCM)LLaMA-3orthopedic surgeryretrieval-augmented generation (RAG)

Identifiers

PMID42064322
PMCPMC13127414

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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.