Clinical Profile and Outcome of 806 Pediatric Oncology Patients Treated With Radiotherapy at the Serbian National Cancer Center
Bibliographic record
Abstract
Radiotherapy plays an important role in the multimodal treatment of childhood cancer. Our objective was to provide an analysis of pediatric oncology patients treated with radiotherapy in a national referral institution in Serbia. A retrospective chart review of children treated with radiotherapy between January 2007 and July 2018 was conducted. Of the 806 patients who were identified, 767 formed the basis of this study. CNS tumors (31.2%) were the most common tumors followed by leukemias (17.3%) and bone tumors (14.3%). The most common indication for radiotherapy was in adjuvant setting (69.1%). Anesthesia or sedation was performed on 115 patients. The 5-year and 10-year overall survival rates were 65.7% and 62.1%, respectively. A significant difference in survival in relation to tumor type was seen. The best survival rates were obtained in patients with retinoblastoma, followed by lymphomas and nephroblastoma, while patients with bone sarcomas had the worst survival. The intent of radiotherapy treatment was also a parameter associated with survival. Patients treated with palliative and definitive intent lived shorter than patients treated with prophylactic and adjuvant intent. Our study showed that good treatment outcomes can be achieved in specialized centers with an experienced team of professionals who are dedicated to pediatric oncology.
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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 | 0.001 | 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".