Prognostic factors for patients with Ewing sarcoma (EWS) at first recurrence following multi‐modality therapy: A report from the Children's Oncology Group
Bibliographic record
Abstract
BACKGROUND: The prognosis for patients with recurrent Ewing sarcoma (EWS) is very poor with 5-year survival of 13%. METHODS: To evaluate prognostic factors for these patients we studied patients initially treated on the multi-institutional study INT0091. RESULTS: Two hundred sixty-two patients experienced disease recurrence. The median time to first recurrence was 1.3 years (0-7.4 years), 1.4 years (0-7.4 years) for patients with initially localized disease and 1 year (0-6 years) for patients with initially metastatic disease. Time to first recurrence from date of initial diagnosis was a predictor of post-recurrence survival (P < 0.0001). Twenty-one percent of patients, with recurrent or progressive disease >or=2 years from initial diagnosis, had an estimated 5-year survival of 30% (vs. 7% estimated 5-year survival with an earlier recurrence). No statistical difference was detected between patients whose disease recurred <1 year and between 1 and 2 years from initial diagnosis. A stepwise relative risk model and backwards stepwise regression was used to explore factors significantly associated with risk for post-recurrence death. Significant risk factors for death after recurrence included recurrence at combined local and distant sites, elevated LDH at initial diagnosis and initial recurrence less than 2 years after diagnosis. Isolated pulmonary recurrence was not predictive of survival after recurrence. CONCLUSION: Patients with a longer disease control interval represent the subset of patients most likely to survive following recurrence of EWS. All patients with recurrence would benefit from collaborative trials to investigate new therapeutic options.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".