Scalp ulcers – differential diagnoses that should be sought!
Bibliographic record
Abstract
BACKGROUND: Ulceration of the scalp is an uncommon clinical presentation, and it may be caused by myriads of cutaneous etiologies such as infections, inflammatory disorders, and malignancies. We sought to reveal the underlying etiology of scalp ulcers referred to our tertiary wound healing clinic; we would also like to propose a classification for scalp ulcerations. METHODS: A retrospective study was conducted in an academic tertiary wound healing clinic between January 2015 and June 2018. The study was approved by the Women's College Hospital Institutional Research Ethics Board. We have also conducted a review of the literature to recognize the major causes of scalp ulceration reported in the literature. RESULTS: We have identified a total number of 15 patients with scalp ulceration. Twelve patients with atypical scalp ulcers underwent a skin biopsy. A malignancy rate of 73% (11/15) was diagnosed histologically. The review of the literature showed 237 articles. After screening the title and the abstracts, we have selected 41 case reports for the full text review. CONCLUSION: Scalp ulcers are uncommon but important. Our sample study indicates the high frequency of malignant etiologies presenting as scalp ulcers. These results emphasize not only the need for clinicians to be on the watch for the possibility of this option but rather highlights the need for early biopsy to prevent further complications. We hope that our paper helps to shed some light on this topic and guide clinicians on how to approach scalp ulceration.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".