Children's Oncology Group's 2023 blueprint for research: Cancer control and supportive care
Bibliographic record
Abstract
The objective of the Cancer Control and Supportive Care (CCL) Committee in the Children's Oncology Group (COG) is to reduce the overall morbidity and mortality of therapy-related toxicities in children, adolescents, and young adults with cancer. We have targeted five major domains that cause clinically important toxicity: (i) infections and inflammation; (ii) malnutrition and metabolic dysfunction; (iii) chemotherapy-induced nausea and vomiting; (iv) neuro- and oto-toxicty; and (v) patient-reported outcomes and health-related quality of life. Subcommittees for each domain prioritize randomized controlled trials and biology aims to determine which strategies best mitigate the toxicities. The findings of these trials are impactful, informing clinical practice guidelines (CPGs) and directly leading to changes in the standard of care for oncology practice. With the development of new therapies, there will be new toxicities, and the COG CCL Committee is dedicated to developing interventions to minimize acute and delayed toxicities, lessen morbidity and mortality, and improve quality of life in pediatric and young adult 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 | 0.033 | 0.065 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.022 | 0.026 |
| Insufficient payload (model declined to judge) | 0.032 | 0.019 |
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".