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Record W4307994798 · doi:10.2196/32546

Concordance and Accuracy of Teledermatology Using Mobile Phones in the Outpatient Clinic of Jose R Reyes Memorial Medical Center: Cross-sectional Study

2022· article· en· W4307994798 on OpenAlexvenueno aff
Ivan Arni C. Preclaro, Zharlah Gulmatico‐Flores, Elizabeth Amelia Velasco Tianco

Bibliographic record

VenueJMIR Dermatology · 2022
Typearticle
Languageen
FieldMedicine
TopicCutaneous Melanoma Detection and Management
Canadian institutionsnot available
Fundersnot available
KeywordsTeledermatologyMedicineConcordanceMedical diagnosisGold standard (test)DermatologyCross-sectional studyOutpatient clinicTelemedicineFamily medicineHealth carePathologyRadiology

Abstract

fetched live from OpenAlex

BACKGROUND: Dermatologists rely on visual findings; thus, teledermatology is uniquely compatible to providing dermatologic care. The use of mobile phones in a store-and-forward approach, where gathered data are sent to a distant health provider for later review, may be a potential bridge in seeking dermatologic care. OBJECTIVE: This study aimed to determine the agreement between face-to-face consultations and teledermatologic consultations through the store-and-forward approach using mobile phones and its accuracy compared to a histopathologic diagnosis. METHODS: The study design was a cross-sectional study of participants consecutively recruited from dermatology patients who presented with skin or mucosal complaint and without prior dermatologist consultation. Photographs were taken using a standard smartphone (iPhone 6s Plus), and a 4-mm skin punch biopsy was taken on each patient-the gold standard to which the study result was compared to. The photographs were sent to 3 consultant dermatologists using a store-and-forward approach, for independent diagnosis and treatment plan. RESULTS: A total of 60 patients were included, with a median age of 41 years. There was moderate-to-almost perfect agreement in terms of final diagnosis between the face-to-face dermatologic diagnosis and teledermatologic diagnoses. The third teledermatologist had the highest agreement with the clinical dermatologist in terms of final diagnosis (κ=0.84; P<.001). Among the 3 dermatologists, there was moderate-to-almost perfect agreement as well. Agreement between pairs of teledermatologists ranged from 0.45 to 0.84. The 3 teledermatologists had moderate-to-substantial agreement with the biopsy results, with the third teledermatologist having the highest accuracy (κ=0.77; P<.001). Overall, there was a moderate agreement in the diagnosis of patients across raters. CONCLUSIONS: Teledermatology is a viable alternative to face-to-face consultations. Our results show moderate-to-substantial agreement in diagnoses from a face-to-face consultation and store-and-forward teledermatology.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.124
Threshold uncertainty score0.550

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.030
GPT teacher head0.369
Teacher spread0.339 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations6
Published2022
Admission routes1
Has abstractyes

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