Evidence-Based Medicine
Bibliographic record
Abstract
LEARNING OBJECTIVES: After studying this article, the participant should be able to: 1. Identify common precancerous and malignant cutaneous growths of the head and neck. 2. Recommend surgical treatment, including margins, based on consensus guidelines. 3. Counsel patients as to available evidence for expected recurrence, follow-up, and morbidity. SUMMARY: Skin lesion excision is the most common procedure performed by plastic surgeons. Because of the cumulative risk factors of sun and carcinogen exposure, the head and neck are the most frequently affected regions of the body. Timely diagnosis and treatment are critical for preventing continued spread and metastasis, and it is incumbent on the treating physician to make the appropriate recommendations for surgical margin and the possibility of adjuvant therapy to prevent recurrence and optimize long-term survival. As clinical guidelines are developed from ongoing outcome studies, new generations of treatment recommendations are continuously in development. Therefore, a systematic review of the most relevant guidelines and clinically rigorous studies was performed with a summarization of treatment recommendations for the following: actinic keratosis, Bowen disease (squamous cell in situ), basal cell carcinoma, squamous cell carcinoma, malignant melanoma, and Merkel cell carcinoma of the head and neck.
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 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 | 0.014 | 0.059 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.046 | 0.009 |
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".