Cutaneous leiomyosarcoma: A rare cutaneous soft tissue neoplasm. Clinicopathologic Features and Review of Literature.
Bibliographic record
Abstract
Leiomyosarcomas of the skin are divided into two subtypes based on their origin and location: superficial dermal leiomyosarcoma believed to originate from the arrector pili muscles and sweat glands, and subcutaneous leiomyosarcoma arising from vascular smooth muscle of subcutaneous adipose tissue. Preoperative misdiagnosis is common because it is a rare malignant tumor, and the diagnosis is based on histopathological and immunohistochemical studies. Although superficial cutaneous leiomyosarcoma is usually treated with surgical excision, high rates of local recurrence (30–50%) have been reported. Subcutaneous leiomyosarcomas tend to be more aggressive, and since they are usually diagnosed at a more advanced stage, they are usually larger than superficial dermal based neoplasms. Recurrence rates are higher at 50–70%, and up to 60% of distant metastases have been reported. Guidelines for surgical management and role of radiation and chemotherapy as adjuvant treatments are not clearly defined. The clinicopathological features of this rare cutaneous soft tissue neoplasm are described, along with a review of the literature. Differential diagnoses, possible histogenesis, clinical behavior, management, and prognostic factors are also discussed.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".