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Record W3135148216 · doi:10.1177/1203475421999326

Overview of Ultrasound Imaging Applications in Dermatology

2021· review· en· W3135148216 on OpenAlex
Nouf Almuhanna, Ximena Wortsman, Iris Wieser, Misaki Kinoshita‐Ise, Raed Alhusayen

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Cutaneous Medicine and Surgery · 2021
Typereview
Languageen
FieldMedicine
TopicCutaneous Melanoma Detection and Management
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
FundersAustrian Science Fund
KeywordsMedicineDermatologyModality (human–computer interaction)UltrasoundClinical PracticeMedical physicsMedical imagingRadiology

Abstract

fetched live from OpenAlex

Complete visualization of lesions is critical for the accurate diagnosis and management of dermatological diseases. Currently, the most readily available technologies used by dermatologists include dermoscopy and photography. Nevertheless, ultrasound has emerged as a useful non-invasive modality in dermatology, which can be added to the clinical examination supporting an early and more accurate diagnosis. Moreover, there are significant technological advances in recent years, such as the development of handheld devices and ultra-high frequency probes that have expanded the integration of ultrasound into daily dermatology practice. In this article, we reviewed the most common applications of ultrasound in the field of dermatology.

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.943
Threshold uncertainty score0.792

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.061
GPT teacher head0.353
Teacher spread0.292 · 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