Clinicodemographic characteristics of extraosseous Ewing sarcoma: A comparative meta-analysis of pediatric and adult patients
Bibliographic record
Abstract
Background: Evidence suggests different presentation patterns and prognosis of extraosseous Ewing Sarcoma (EES) based on age. Thus, we carried out this study to test the difference between children and adult EES cases regarding clinicodemographic characteristics and prognosis. Methods: A total of 4 databases were explored yielding 18 relevant studies for data synthesis. Outcomes included the comparison of demographic and clinical characteristics as well as prognosis between children and adults with EES. Log odds ratio (logOR) and its 95% confidence interval (CI) were pooled across studies. Statistical models/methods were selected based on heterogeneity. Results: Our analysis included a total of 1261 children and 1256 adults. When we compared these two age categories, we did not observe a significant difference in the risk of developing EES [logOR = -0.13; 95% CI: -0.65: 0.39; I2 = 88.42%]. No significant differences regarding gender, tumor location, and size (≤5 vs. >5 cm), EWSR1 positivity, or management modality. We did not observe significant difference regarding clinical outcomes, such as 5-year overall survival and event-free survival, recurrence, mortality, no evidence of disease, and secondary metastasis. Conclusions: Our findings highlight the absence of an association between the age category of patients and the incidence of EES, as well as its clinical outcomes.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.011 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".