Head and neck adnexal skin cancers
Bibliographic record
Abstract
PURPOSE OF REVIEW: Adnexal carcinomas (AC) are rare skin lesions predominantly affecting elderly individuals. These tumors are often located in the head and neck region and are influenced by factors such as sun exposure, prior radiation therapy, and immunosuppression. Understanding the pathogenesis and management of AC is crucial for improving patient outcomes. RECENT FINDINGS: AC may arise de novo or from preexisting benign lesions. They may act as cutaneous markers for hereditary syndromes, highlighting the need for their early identification. Accurate diagnosis is critical, requiring adequate biopsy for proper characterization, as superficial excisions may lead to mistakes. Surgical treatment remains the primary approach, with wide (at least 1 cm) surgical margins also recommended for lesions with lower malignancy potential. Mohs surgery is particularly useful for tumors located in cosmetically sensitive areas, offering precise resection and clear margins. SUMMARY: AC are classified into good, intermediate or poor prognosis categories based on the risk of local recurrence and distant metastasis. This classification assists in determining the need for adjuvant treatments and follow-up strategies. The proper understanding of risk factors, pathogenesis, and treatment options is essential to improve outcomes and ensure optimal management of AC.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".