Local Recurrence After Initial Multidisciplinary Management of Soft Tissue Sarcoma: Is there a Way Out?
Bibliographic record
Abstract
BACKGROUND: Multimodality treatment of primary soft tissue sarcoma by expert teams reportedly affords a low incidence of local recurrence. Despite advances, treatment of local recurrence remains difficult and is not standardized. QUESTIONS/PURPOSES: We (1) determined the incidence of local recurrence from soft tissue sarcoma; (2) compared characteristics of the recurrent tumors with those of the primary ones; (3) evaluated local recurrences, metastases and death according to treatments; and (4) explored the relationship between the diagnosis of local recurrence and the occurrence of metastases. METHODS: From our prospective database, we identified 618 soft tissue sarcomas. Thirty-seven of the 618 patients (6%) had local recurrence. Leiomyosarcoma was the most frequent diagnosis (eight of 37). The mean delay from original surgery was 22 months (range, 2-75 months). Mean size was 4.8 cm (range, 0.4-28.0 cm). Median followup after local recurrence was 16 months (range, 0-98 months). RESULTS: Recurrent tumors had a tendency toward becoming deeper seated and higher graded. Nineteen of the 37 patients with recurrence underwent limb salvage (nine free flaps) and six had an amputation. Twenty-two (59%) had metastases, including 10 occurring after the local recurrence event at an average delay of 21 months (range, 1-34 months). Six patients developed additional local recurrences, with no apparent difference in risk between amputation (two of six) and limb salvage (four of 19). CONCLUSIONS: Patients with a local recurrence of a soft tissue sarcoma have a poor prognosis. Limb salvage and additional radiotherapy remain possible but with substantial complications. Amputation did not prevent additional local recurrence or death.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".