Preoperative Risk Factors for Fibrosarcomatous Transformation in Dermatofibrosarcoma Protuberans
Bibliographic record
Abstract
BACKGROUND/AIM: Dermat of ibrosarcoma protuberans (DFSP) is a soft-tissue sarcoma with a high risk of local recurrence, though typically never metastasizes. DFSP can transform into high-grade fibrosarcoma (DFSP-FS), which has a risk of metastasis. Currently, treatment for DFSP includes Moh's micrographic surgery (MMS); however, this is not recommended for DFSP-FS. Often, the transformation to DFSP-FS is not recognized until the final histological diagnosis. At that point, wide local excision (WLE) of a previous MMS site can be morbid. As such, we analyzed patient risk factors to allow identification of DFSP-FS transformation at presentation. PATIENTS AND METHODS: We reviewed 368 (174 female, 194 male) patients with a mean age of 42 years from two sarcoma centers. A total of 319 (87%) patients had a history of DFSP and 49 (13%) had DFSP-FS. RESULTS: When comparing patients with a DFSP to those with a DFSP-FS, patients with a DFSP-FS were more likely (p<0.05) to be older, female and with larger tumors. A painful mass and rapidly enlarging mass were associated with DFSP-FS. CONCLUSION: Patients who presented with DFSP-FS were found to typically have a larger, painful, and growing mass. Patients with these features should be referred for WLE over MMS at presentation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.003 | 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 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".