Efficacy and survival of 92 cases of Ewing's sarcoma family of tumor initially treated with multidisciplinary therapy
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Ewing's sarcoma family of tumor (ESFT) is aggressive. The optimal therapy modality for ESFT is still to be found. This study was to explore the clinical characteristics and therapy for ESFT. METHODS: Ninety-two cases of ESFT were collected from January 1995 to April 2008 in Sun Yat-sen University Cancer Center and analyzed retrospectively. RESULT: Of 92 cases, 23 were Ewing's sarcoma of bone, 21 extraosseous Ewing's sarcoma, 43 peripheral primitive neuroectodermal tumor, and 5 Askin tumor. Median follow-up time was 31.5 months (range, 10-137 months). Thirty-eight patients received multidisciplinary therapy and 19 single model therapy in non-metastasis group. Three-year overall survival (OS) and event-free survival (EFS) were significantly different between non-metastatic multidisciplinary therapy group and non-metastatic single model group (63% vs. 20%, 46% vs. 18%, respectively, P<0.001). The patients who received surgery plus chemotherapy and plus radiation or not had longer survival than those treated with chemotherapy plus radiation in non-metastatic multidisciplinary therapy group (Chi2=7.591, 9.212; P=0.006, 0.002). CAV/IE alternative regimen was superior to other regimens in event-free survival, but not in overall survival (Chi2=6.950, 3.530; P=0.008, 0.06). Cox regression analysis suggested therapy model and response to treatment were independent prognostic factors for ESFT. CONCLUSIONS: Our studying showed multidisciplinary therapy could significantly improve non-metastatic ESFT patients' survival. Chemotherapy plus surgery and plus radiation or not were superior to chemotherapy plus radiation in local control for the non-metastatic ESFT. Therapy model and response were independent prognostic factors.
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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) | 0.000 | 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 | 0.000 |
| 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".