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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".