Low-grade Nonrhabdomyosarcoma Soft Tissue Sarcoma: What is Peculiar for Childhood
Bibliographic record
Abstract
BACKGROUND: Nearly half of soft tissue sarcomas are nonrhabdomyosarcomas (NRSTSs). The low-grade (LG) form comprises a heterogenous group of diseases that rarely metastasize but are known for local recurrence. AIM OF THE STUDY: The aim of the study was to retrospectively evaluate pediatric LG-NRSTS with regard to demography, survival, and factors affecting outcome in Egyptian patients. PATIENTS AND METHODS: The study reviewed 66 NRSTS patients who presented to the Pediatric Oncology Department, National Cancer Institute, Cairo University, between January 2008 and December 2013. RESULTS: Out of the reviewed cases 32 patients had LG tumors and were eligible for analysis. The male to female ratio was 1:1 and the median age was 7.5 years (range, 1 mo to 18 y). Desmoid fibromatosis (N=18) showed frequent local recurrence and nearly half of this group was alive without disease. No recurrence of the disease occurred in the nonfibromatosis group (n=14) and all patients were alive and free of disease. The 5-year overall survival was 88% for the entire group of study patients versus 45% for event-free survival. Tumors >5 cm in diameter and fibromatosis histology subtype were associated with lower EFS. CONCLUSIONS: LG-NRSTS generally has good prognosis, with overall survival reaching 90%. However, aggressive fibromatosis usually runs a poorer course in the form of high incidence of local recurrence and lower survival rates. This needs to be further assessed in larger prospective studies including novel therapies in addition to the current conventional modalities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".