A guide for dermatology nurses to assist in the early detection of skin cancer
Bibliographic record
Abstract
Early diagnosis of skin cancer, particularly melanoma, leads to improved morbidity and mortality. While nurses have been leaders in skin cancer awareness and education for decades, the nursing community can take a more active role in the fight against skin cancer. In order to assume this role, nurses must be familiar with diagnostic aids that help in the early recognition of skin cancer. Dermatology nurses facilitate care in the interdisciplinary team by focusing on patient centered outcomes. Nursing roles and responsibilities in the interdisciplinary team are vital to clinic pre-screening, improving public awareness, disseminating patient education, providing guidance regarding sun avoidance and protection, and providing education on the fundamentals of skin self-examinations and total body skin examinations. Nursing skin assessment requires knowledge of skin lesion morphology and biology, and pattern recognition. As the sensitivity and specificity of naked eye examinations are suboptimal, dermoscopy provides a method for improving and streamlining skin lesion triage and assessment. In this review, we discuss a multi-prong approach to the diagnosis of melanoma, including the ABCDE mnemonic, the “ugly duckling” concept, and some newer technologies ( e.g. , dermoscopy and total body photography) that aid in the early detection of skin cancers. Familiarity with these detection aids can provide nurses with a basic framework to aid in diagnosing skin cancer.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.074 | 0.069 |
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".