Evidence map›Paper›PMID 40398852›Full record

ArticleApplied clinical informatics2025

Extracting Social Determinants of Health from Dental Clinical Notes.

Farhana Pethani, Alec Chapman, Mike Conway, Xiang Dai, Demiana Bishay, Victor Choh, Alexander He, Su-Elle Lim, Huey Ying Ng, Tanya Mahony and 4 more

Abstract read
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Article in Applied clinical informatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Using reasoning LLMs to extract SDOH events from clinical notes.Proceedings. IEEE International Conference on Healthcare Informatics · 2026
    Article
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

14 authors.

Farhana PethaniBiomedical Informatics and Digital Health, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia.
Alec ChapmanInformatics, Decision-Enhancement and Analytic Sciences Center, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, Utah, United States.
Mike ConwaySchool of Computing and Information Systems, The University of Melbourne, Melbourne, Australia.
Xiang DaiData61, Commonwealth Scientific and Industrial Research Organisation, Sydney, Australia.
Demiana BishayThe University of Sydney School of Dentistry, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia.
Victor ChohThe University of Sydney School of Dentistry, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia.
Alexander HeThe University of Sydney School of Dentistry, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia.
Su-Elle LimThe University of Sydney School of Dentistry, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia.
Huey Ying NgThe University of Sydney School of Dentistry, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia.
Tanya MahonyOral Health Services, Nepean Blue Mountains Local Health District, Penrith, Australia.
Albert YaacoubThe University of Sydney School of Dentistry, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia.
Sarvnaz KarimiData61, Commonwealth Scientific and Industrial Research Organisation, Sydney, Australia.
Heiko SpallekThe University of Sydney School of Dentistry, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia.
Adam G DunnBiomedical Informatics and Digital Health, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In dentistry, social determinants of health (SDoH) are potentially recorded in the clinical notes of electronic dental records. The objective of this study was to examine the availability of SDoH data in dental clinical notes and evaluate natural language processing methods to extract SDoH from dental clinical notes.A set of 1,000 dental clinical notes was sampled from a dataset of 105,311 patient visits to a dental clinic and manually annotated for information pertaining to sugar, tobacco, alcohol, methamphetamine, housing, and employment. Annotations included temporality, dose, type, duration, and frequency where appropriate. Experiments were to compare extraction using fine-tuned pretrained language models (PLMs) with a rule-based approach. Performance was measured by F1-score.For identifying SDoH, the best-performing PLM method produced F1-scores of 0.75 (sugar), 0.69 (tobacco), 0.67 (alcohol), 0.42 (housing), and 0 (employment). The rule-based method produced F1-scores of 0.70 (sugar), 0.69 (tobacco), 0.53 (alcohol), 0.44 (housing), and 0 (employment). The overall difference between PLMs and rule-based methods was F1-score of 0.04 (95% confidence interval -0.01, 0.09). SDoH were relatively rare in dental clinical notes, from sugar (9.1%), tobacco (3.9%), alcohol (1.2%), housing (1.2%), employment (0.2%), and methamphetamine use (0%).The main challenge of extracting SDoH information from dental clinical notes was the frequency with which they are recorded, and the brevity and inconsistency where they are recorded. Improved surveillance likely needs new ways to standardize how SDoHs are reported in dental clinical notes.

Indexed as

Dental RecordsElectronic Health RecordsSocial Determinants of HealthHumansNatural Language Processing

Identifiers

PMID40398852
PMCPMC12494450

What OpenQuestion holds

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Registered trials

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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.