Bibliographic record
Abstract
The survival of children with cancer has increased dramatically in the last decades, as a result of advances in diagnosis, treatment and supportive care. Each year in Turkey, 2500-3000 new childhood cancer cases are expected. According to the Turkish Pediatric Oncology Group and Turkish Pediatric Hematology Societies Registry, about 2000 new pediatric cancer cases are reported each year. The population in Turkey is relatively young. One fourth of the population is younger than 15 years of age. According to childhood mortality, cancer is the fourth cause of death (7.2%) after infections, cardiac deaths and accidents. The major cancers in children in Turkey are leukemia (31%), lymphoma (19%), central nervous system (CNS) neoplasms (13%), neuroblastomas (7%), bone tumors (6.1%), soft tissue sarcomas (6%), followed by renal tumors, germ cell tumors, retinoblastoma, carcinomas-epithelial neoplasms, hepatic tumors and others. Lymphomas rank second in frequency as in many developing countries in contrast to West Europe or USA, where CNS neoplasms rank second in frequency. The seven-year survival rate in children with malignancies in Turkey is 65.8%. The history of modern Pediatric Oncology in Turkey dates back to the 1970's. Pediatric Oncology has been accepted as a subspecialty in Turkey since 1983. Pediatric Oncologists are all well trained and dedicated. All costs for the diagnosis and treatment of children with cancer is covered by the government. Education and infrastructure for palliative care needs improvement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.005 |
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".