Consensus Recommendations From the Children’s Oncology Group Nursing Discipline’s State of the Science Symposium: Symptom Assessment During Childhood Cancer Treatment
Bibliographic record
Abstract
Background: Recognizing and addressing illness-related distress has long been a priority for pediatric oncology nurses and the Children’s Oncology Group. Although symptoms are known to be highly prevalent during treatment for childhood cancer, there is currently no guidance for how often symptoms should be assessed, which symptoms should be prioritized for assessment, and how the data should be collected. Methods: The Nursing Discipline, within Children’s Oncology Group, hosted a one-day Interprofessional seminar titled “Symptom Assessment During Childhood Cancer Treatment: State of the Science Symposium.” Following the symposium, an expert panel was assembled to review all available evidence, including information presented and collected during the symposium. Consensus-building discussions were held to identify common themes and to produce recommendations for clinical practice. Results: Four recommendations emerged including (1) the identification of priority “core” symptoms for assessment; (2) inclusion of the child’s voice through self-report, when possible; (3) consistent documentation and communication of symptom assessment results; and (4) implementation of patient/family education related to symptoms. Discussion: Symptom recognition, through appropriate assessment, is the first step in symptom management. The goal for developing and sharing these recommendations is to promote consistent and comparable clinical practice across institutions in regard to symptom assessment during childhood cancer therapy. Integration of these recommendations will set the stage for future studies related to the frequency of symptoms across disease groups, projection of anticipated symptom trajectories, development of evidence-based teaching tools for common symptoms, and evaluation of patient outcomes with enhanced symptom assessment and management.
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 | 0.175 | 0.253 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.011 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.014 | 0.016 |
| Research integrity | 0.033 | 0.034 |
| Insufficient payload (model declined to judge) | 0.010 | 0.009 |
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".