Bibliographic record
Abstract
ABSTRACTDespite the fact that the majority of childhood cancer cases occur in the developing world, pediatric oncology in developing countries has not been afforded the ground breaking advances and successes that are available in developed countries. It is an underestimated global child health concern, and the factors contributing to the two-tiered cancer outcome profile between developed and developing countries are complex and expansive. There are some initiatives in place, such as the twinning program, that are successfully improving cancer treatment in resource-limited regions, but more international advocacy is needed to make state of the art cancer therapy available to all children. RÉSUMÉBien que la majorité des cas de cancers pédiatriques se produisent dans des pays en voie de développement, l’oncologie pédiatrique dans ces pays n’a pas pu profiter des avancées révolutionnaires qui sont accessibles dans les pays développés. Cela est un problème de santé pédiatrique mondiale sous-estimé, et les facteurs contribuant au profil à deux paliers des taux de survie du cancer entre les pays développés et ceux en voie de développement sont complexes et de grande ampleur. Il y a des initiatives en place, tels les programmes de jumelage, qui améliorent avec succès le traitement du cancer dans les régions possédant des ressources limitées, mais plus de défense internationale des droits des enfants est nécessaire pour assurer la disponibilité des toutes dernières thérapies contre le cancer pour tous les enfants.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".