Interventions to reduce the risk of side-effects of cancer treatments in childhood
Bibliographic record
Abstract
INTRODUCTION: Childhood cancers as a group affect around 1 in 500 children but each individual diagnosis is a rare disease. While research largely focuses on improving cure rates, the management of side effects of treatment are high priority for clinicians, families and children and young people. AREAS COVERED: The prevention and efficient management of infectious complications, oral mucositis, nausea and vomiting and graft-vs-host disease illustrated with examples of implementation research, translation of engineering to care, advances in statistical methodologies, and traditional bench-to-patient development. The reviews draw from existing systematic reviews and well conducted clinical practice guidelines. EXPERT OPINION: The four areas are driven from patient and family priorities. Some of the problems outlined are ready for proven interventions, others require us to develop new technologies. Advancement needs us to make the best use of new methods of applied health research and clinical trial methodologies. Some of the greatest challenges may be those we're not fully aware of, as new therapies move from their use in adult oncological practice into children. This will need us to continue our collaborative, multi-professional, multi-disciplinary and eclectic approach.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| 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) | 0.000 | 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 teacher head, 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".