The importance of palpation in the skin cancer screening examination
Bibliographic record
Abstract
With the recent unprecedented shift toward contactless healthcare solutions, providers should recall the value that proper palpation adds to dermatologic practice. We present a case that demonstrates the limitations of touchless care and how proper palpation during skin cancer examinations may impact cosmetic outcomes. Our patient is an 86-year-old male patient with Sezary syndrome and monoclonal B-cell lymphocytosis whose squamous cell carcinoma invasion was missed by visual inspection alone. He delayed treatment of his biopsy-proven squamous cell carcinoma for 15 months. On follow-up, visual examination only showed a well-healed biopsy scar, and treatment was delayed another 2 months. Finally, thorough physical examination found perineural invasion. This helped guide the Mohs approach, but due to the delays resulted in a larger final defect and poorer cosmetic outcomes. Proper, deep palpation of skin lesions, especially prior biopsy sites, is imperative to the treatment of skin cancer in cosmetically sensitive areas. Biopsy scars on the face often heal well, and visual only inspection may miss crucial details. This case also reminds dermatologists of the importance of patient education in the prompt treatment of skin cancer for the best cosmetic outcomes.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".