Evidence map›Paper›PMID 41044148›Full record

ArticleNPJ digital medicine2025

Validity of two subjective skin tone scales and its implications on healthcare model fairness.

Cassandra W Cu, Nicole E Dundas, Timothy Heintz, Zahida A Sheikh, Bianca Alonso-Bermudez, Jasmine Walker, Avery Wooten, Anusha Badathala, Allyson Chapman, Odinakachukwu Ehie and 6 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NPJ digital medicine, 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. 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

16 authors.

Cassandra W Cu *School of Medicine, Tufts University School of Medicine, Boston, MA, USA.
Nicole E Dundas *UC Berkeley Department of Bioengineering, Berkeley, CA, USA.
Timothy HeintzDepartment of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women's Hospital, Boston, MA, USA.
Zahida A SheikhSchool of Medicine, Tufts University School of Medicine, Boston, MA, USA.
Bianca Alonso-BermudezSchool of Medicine, Tufts University School of Medicine, Boston, MA, USA.
Jasmine WalkerSchool of Medicine, Tufts University School of Medicine, Boston, MA, USA.
Avery WootenSchool of Medicine, University of California, San Francisco, San Francisco, CA, USA.
Anusha BadathalaDivision of Anesthesia, San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA.
Allyson ChapmanCritical Care and Palliative Medicine, Department of Internal Medicine, University of California San Francisco, San Francisco, CA, USA.
Odinakachukwu EhieDepartment of Anesthesia and Perioperative Care, University of California San Francisco, San Francisco, CA, USA.
Karthik RaghunathanDepartment of Anesthesia and Perioperative Care, Duke University, Durham, NC, USA.
Hunter MillsBakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA.
Edie EspejoDivision of Geriatrics, Department of Medicine, University of California, San Francisco, San Francisco, CA, USA.
John BoscardinDivision of Geriatrics, Department of Medicine, University of California, San Francisco, San Francisco, CA, USA.
Arthur W WallaceDivision of Anesthesia, San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA.
Julien CobertDivision of Anesthesia, San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA. Julien.cobert@ucsf.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Skin tone assessments are critical for fairness evaluation in healthcare algorithms (e.g., pulse oximetry) but lack validation. Using prospectively collected facial images from 90 hospitalized adults at the San Francisco VA, three independent annotators rated facial regions in triplicate using Fitzpatrick (I-VI) and Monk (1-10) skin tone scales. Patients also self-identified their skin tone. Annotator confidence was recorded using 5-point Likert scales. Across 810 images in 90 patients (9 images each), within-rater agreement was high, but inter-annotator agreement was moderate to low. Annotators frequently rated patients as darker when patients self-identified as lighter, and lighter when patients self-identified as darker. In linear mixed-effects models controlling for facial region and annotator confidence, darker self-reported skin tones were associated with lighter annotator scores. These findings highlight challenges in consistent skin tone labeling and suggest that current methods for assessing representation in biosensor-based algorithm studies may be influenced by labeling bias.

Identifiers

PMID41044148
PMCPMC12494915

What Socratic holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

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.