Treatment outcome of pediatric rhabdomyosarcoma at national cancer institute, Egypt
Bibliographic record
Abstract
Background Rhabdomyosarcoma (RMS) is the most common soft tissue sarcoma in infants and children. It is the third most common solid tumor in children after neuroblastoma and Wilms tumor, making up 10–15% of all solid pediatric tumors. At National Cancer Institute (NCI), Egypt, soft tissue sarcomas represent 3.75% of total malignancies and 27.6% of these occur in the pediatric group. RMS is the most common type. Aim and objectives This work aims to study the treatment outcome, overall survival (OS), and event free survival (EFS) of pediatric RMS patients diagnosed and treated at NCI. Patients and methods This is a retrospective study that included 54 pediatric patients, newly diagnosed with RMS who were treated at the pediatric oncology department, NCI, Cairo University, Egypt during the period from January 2012 to December 2016. Results Totally 54 pediatric patients with RMS with ages ranging from 7 months to 17 years (median age 5 years) were studied. The median follow-up period ranged with a minimum 1 year for the last patient. In this study, we classify our patients into low, intermediate, and high-risk groups according to IRS and we found that 11 (20.4%) patients were eligible for the low-risk group, 27 (50%) patients were eligible for the intermediate risk group and 16 (29.6%) patients were eligible for the high-risk group. The 2-year OS for low-risk group was 90.9%, it was 52.1% for intermediate-risk group, while it was 43.8% for high-risk group (P=0.02). The 2-year EFS for low-risk group was 63.6%, it was 41.2% for intermediate-risk group, while it was 31.3% for high-risk group (P=0.203). Conclusion RMS requires combined-modality therapy. Late presentation and advanced local disease compromise treatment options and decrease OS and EFS.
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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 | 0.001 |
| 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".