Bibliographic record
Abstract
There are significant differences between childhood and adult cancers in a number of ways, including causes, treatments, and response to treatment. Childhood cancers are often associated with factors such as germline predisposition, genetic mutations, viral infections, and exposure to toxic substances. In contrast, adult cancers may be more influenced by factors such as family history, long-term exposure to radiation, and poor lifestyle habits. Although both childhood and adult cancers may be treated with chemotherapy, radiotherapy, and immunotherapy, treatment for children requires special attention to dose optimization and minimization of long-term side effects. In addition, the treatment of pediatric cancers presents some special challenges. The relatively low incidence of childhood cancers, the large tumor heterogeneity, and the fact that the needs and responses of each child may be different have led to relatively slow progress in pediatric oncology research. At the same time, due to the immature functioning of body systems in children during development, treatment requires more careful consideration of various possible side effects and implications. Therefore, this article first analyzes the differences in the causes of cancer between children and adults, then, explores the differences in cancer treatment between the two, and finally, emphasizes the need for collaborative efforts to address these challenges, improve access to care, and increase survival rates for pediatric patients with cancer.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.003 | 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".