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Early Malignant Melanoma Detection, Especially in Persons with Pigmented Skin

2023· article· en· W4317567798 on OpenAlexaff
Paul Andrei Jicman, Hiske Smart, Elizabeth A. Ayello, R. Gary Sibbald

Bibliographic record

VenueAdvances in Skin & Wound Care · 2023
Typearticle
Languageen
FieldMedicine
TopicCutaneous Melanoma Detection and Management
Canadian institutionsWomen's College HospitalUniversity of Toronto
Fundersnot available
KeywordsMedicineMelanomaDermatologyMelanoma diagnosisCancer research

Abstract

fetched live from OpenAlex

ABSTRACT Early detection of malignant melanoma is associated with better survival and clinical outcomes. Visual skin inspection is part of melanoma lesion assessment, but clinicians often have difficulty identifying lesions in persons with darker skin tones (eg, Fitzpatrick type 5 [brown] and type 6 [black] skin). There is also a lack of knowledge about the skin sites that are best to evaluate in persons with darkly pigmented skin (eg, the plantar surface of the feet, palms of the hand, and under the nail plate). These limitations can lead to a delay in diagnosis with potentially poor prognostic outcomes. In this article, the authors identify relevant literature to increase awareness for the presence of early signs of malignant melanoma in all skin types. Patient empowerment includes lifestyle adaptations, such as conducting regular skin and foot self-examinations to detect melanoma signs and applying sun protection on feet. GENERAL PURPOSE To present a comprehensive gap analysis of podiatric melanoma literature. TARGET AUDIENCE This continuing education activity is intended for physicians, physician assistants, nurse practitioners, and nurses with an interest in skin and wound care. LEARNING OBJECTIVES/OUTCOMES After participating in this educational activity, the participant will:1. Select the appropriate assessment techniques for screening patients, especially those with skin of color, for melanoma.2. Compare and contrast the various types of melanoma.3. Discuss the results of the literature review that offer insight to clinicians screening patients for melanoma.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.701
Threshold uncertainty score0.931

Codex and Gemma teacher scores by category

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

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 head0.007
GPT teacher head0.250
Teacher spread0.244 · 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 designOther design
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

Citations4
Published2023
Admission routes1
Has abstractyes

Explore more

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