Early Malignant Melanoma Detection, Especially in Persons with Pigmented Skin
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".