Optimizing symptom control in children and adolescents with cancer
Bibliographic record
Abstract
There is growing recognition of the degree to which symptoms negatively impact on children receiving cancer treatments. A recent study described that almost all inpatient pediatric oncology patients are experiencing at least one bothersome symptom and almost 60% are experiencing at least one severely bothersome symptom. Poor symptom control occurs because of challenges with communication of bothersome symptoms to clinicians, lack of clinical practice guidelines (CPGs) for most of these symptoms, and failure to administer preventative and therapeutic interventions known to be effective for symptom control. This article reviews approaches used to improve symptom control for children receiving cancer treatments. Areas addressed include systematic symptom screening and creation of CPGs for symptom management. Challenges with electronic health integration are also addressed. Several multi-symptom assessment scales have been developed but none have yet been used to directly influence patient management. The number of CPGs applicable to symptom control in pediatric oncology is increasing but remains small. Improving the creation of and adherence to CPGs for symptom management is an important priority. Finally, identifying ways that symptom management systems can be integrated into clinical work flows is essential; these will likely need to focus on electronic health records.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".