Evidence map›Paper›PMID 40206682›Full record

ArticleMayo Clinic proceedings. Digital health2024

Differences Between Patient and Clinician-Taken Images: Implications for Virtual Care of Skin Conditions.

Rajeev V Rikhye, Grace Eunhae Hong, Preeti Singh, Margaret Ann Smith, Aaron Loh, Vijaytha Muralidharan, Doris Wong, Rory Sayres, Michelle Phung, Nicolas Betancourt and 13 more

Abstract read
In one paragraph

Article in Mayo Clinic proceedings. Digital health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Trial
  2. Article
  3. Observational
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

23 authors.

Rajeev V RikhyeGoogle Health, Mountain View, CA.
Grace Eunhae HongStanford University School of Medicine, Stanford, CA.
Preeti SinghGoogle Health, Mountain View, CA.
Margaret Ann SmithStanford University School of Medicine, Stanford, CA.
Aaron LohGoogle Health, Mountain View, CA.
Vijaytha MuralidharanStanford University School of Medicine, Stanford, CA.
Doris WongGoogle Health, Mountain View, CA.
Rory SayresGoogle Health, Mountain View, CA.
Michelle PhungStanford University School of Medicine, Stanford, CA.
Nicolas BetancourtStanford University School of Medicine, Stanford, CA.
Bradley FongStanford University School of Medicine, Stanford, CA.
Rachna SahasrabudheStanford University School of Medicine, Stanford, CA.
Khoban NasimStanford University School of Medicine, Stanford, CA.
Alec EschholzStanford University School of Medicine, Stanford, CA.
Yossi MatiasGoogle Health, Mountain View, CA.
Greg S CorradoGoogle Health, Mountain View, CA.
Katherine ChouGoogle Health, Mountain View, CA.
Dale R WebsterGoogle Health, Mountain View, CA.
Peggy BuiGoogle Health, Mountain View, CA.
Yuan LiuGoogle Health, Mountain View, CA.
Yun LiuGoogle Health, Mountain View, CA.
Justin KoStanford University School of Medicine, Stanford, CA.
Steven LinStanford University School of Medicine, Stanford, CA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To understand and highlight the differences in clinical, demographic, and image quality characteristics between patient-taken (PAT) and clinic-taken (CLIN) photographs of skin conditions. Patients and Methods: This retrospective study applied logistic regression to data from 2500 deidentified cases in Stanford Health Care's eConsult system, from November 2015 to January 2021. Cases with undiagnosable or multiple conditions or cases with both patient and clinician image sources were excluded, leaving 628 PAT cases and 1719 CLIN cases. Demographic characteristic factors, such as age and sex were self-reported, whereas anatomic location, estimated skin type, clinical signs and symptoms, condition duration, and condition frequency were summarized from patient health records. Image quality variables such as blur, lighting issues and whether the image contained skin, hair, or nails were estimated through a deep learning model. Results: Factors that were positively associated with CLIN photographs, post-2020 were as follows: age 60 years or older, darker skin types (eFST V/VI), and presence of skin growths. By contrast, factors that were positively associated with PAT photographs include conditions appearing intermittently, cases with blurry photographs, photographs with substantial nonskin (or nail/hair) regions and cases with more than 3 photographs. Within the PAT cohort, older age was associated with blurry photographs. Conclusion: There are various demographic, clinical, and image quality characteristic differences between PAT and CLIN photographs of skin concerns. The demographic characteristic differences present important considerations for improving digital literacy or access, whereas the image quality differences point to the need for improved patient education and better image capture workflows, particularly among elderly patients.

Identifiers

PMID40206682
PMCPMC11975730

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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.