Desmoid tumors: A novel approach for local control
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: After resection, desmoid tumors are associated with a recurrence rate that is typically 25-50%. Although this is an unusual problem, we instituted a prospective cohort study with neoadjuvant chemotherapy and radiation, followed by surgical resection, in an effort to improve local control. METHODS: Between 1985 and 1999, 13 patients with potentially resectable disease were managed with a treatment protocol of preoperative doxorubicin (30 mg continuous infusion daily for 3 days) and radiotherapy (10 x 300 cGy). Resection was performed 4-6 weeks later. All lesions were resected with an intended margin of 1 cm, but clear adventitial margins were accepted in order to preserve critical structures. RESULTS: The median follow-up was 71 months (range, 22-109). Six patients (46%) presented after failure of a previous surgery. Clear microscopic margins were obtained in 11 patients, and 2 patients had positive margins. There were two local recurrences (15% local recurrence). Both recurrences followed resection of large thigh lesions, which appeared at 30 and 49 months of follow-up. In one patient with a chest wall tumor, two new primary desmoid tumors developed outside the treatment area, in the ipsilateral arm and forearm. Eleven patients have been disease free for a median of 71 months (range, 22-109). CONCLUSIONS: For potentially resectable lesions, this protocol provides excellent local control, even in those with recurrent disease. Neoadjuvant treatment with doxorubicin and radiotherapy appears to be a better option than surgery alone, or surgery and adjuvant radiotherapy. These results need to be confirmed in larger, prospective randomized trials.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 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".