Bibliographic record
Abstract
Cancer affects 1 in every 500 children before the age of 14. Little is known about the etiology of this heterogeneous group of diseases despite the fact that they constitute the major cause of death by disease among this population. Because of its relatively higher prevalence, most of the work done in pediatric oncogenetics has been focused on leukemias, particularly acute lymphoblastic leukemia (ALL). Although it is now well accepted that genetic variations play a significant role in determining individual's cancer susceptibility, few studies have explored genetic susceptibility to childhood leukemia with respect to polymorphisms. The main biological mechanisms contributing to cancer susceptibility can be grouped into broad categories : (1) cellular growth and differentiation, (2) DNA replication and repair, (3) xenobiotic metabolism, (4) apoptosis, (5) oxidative stress response and (6) cell cycle. To evaluate whether candidate genes in these pathways are involved in childhood leukemogenesis, we conducted association studies. We showed that leukemogenesis in children may be associated with genetic variants and that the combination of genotypes seems to be more predictive of risk than either of them independently. These results indicate that the genetic investigation of several enzymes (or metabolic pathways) is needed to explain the physiopathology of childhood leukemia because of the complexity of the environment and that of the inter-individual variability in cancer susceptibility.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.006 |
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; both teacher heads agree on what is shown here.
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".