Phase 1 adaptive dose‐finding study of neoadjuvant gemcitabine combined with radiation therapy for patients with high‐risk extremity and trunk soft tissue sarcoma
Bibliographic record
Abstract
BACKGROUND: This study was performed to determine the maximum tolerated dose (MTD) of gemcitabine given concurrently with preoperative, fixed-dose external-beam radiation therapy (EBRT) for patients with resectable, high-risk extremity and trunk soft tissue sarcoma (STS). METHODS: Gemcitabine was administered on days 1, 8, 22, 29, 43, and 50 with EBRT (50 Gy in 25 fractions over 5 weeks). The gemcitabine MTD was determined with a toxicity severity weight method (TSWM) incorporating 6 toxicity types. The TSWM is a Bayesian procedure that choses each cohort's dose to have a posterior mean total toxicity burden closest to a predetermined clinician-defined target. Clinicopathologic and outcome data were also collected. RESULTS: Thirty-six patients completed the study. According to the TSWM, the gemcitabine MTD was 700 mg/m(2). At this dose level, 4 patients (24%) experienced grade 4 toxicity; no toxicity-related deaths occurred. All tumors were resected with microscopically negative margins. Pathologic responses of >90% tumor necrosis were achieved in 17 patients (47%); 14 (39%) had complete responses. With a median follow-up of 6.2 years, the 5-year locoregional recurrence-free survival, distant metastasis-free survival, and overall survival rates were 85%, 80%, and 86%, respectively. CONCLUSIONS: The TSWM combines data from qualitatively different toxicities and can be used to determine the MTD for a drug given as part of a multimodality treatment. Neoadjuvant gemcitabine plus radiation therapy is feasible and safe in patients with high-risk extremity and trunk STS. Major pathologic responses can be achieved, and after complete resection, long-term clinical outcomes are encouraging.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".