Improving symptom control and reducing toxicities for pediatric patients with hematological malignancies
Bibliographic record
Abstract
The continuing improvement in pediatric cancer survival over time is largely attributable to the availability of intensive therapies. Increasing attention has been focused on addressing the physical and psychosocial impacts of cancer and cancer treatments. Evidence from adult oncology suggests that routine symptom screening and feedback to health care providers can improve patient-clinician communication, reduce distress, and improve quality of life and may even increase survival. Many questions remain regarding implementation of routine symptom screening in pediatric cancer care, including the best symptom assessment instrument and the reporter type and feasibility of integration with electronic health records (EHRs). Nonsymptom adverse events are also important, for both routine clinical care and adverse event reporting for patients enrolled in clinical trials. However, traditional mechanisms for reporting adverse events lead to substantial inaccuracies and are labor intensive. An automated approach for abstraction from EHRs is a potential mechanism for improving accuracy and reducing workload. Finally, identification of symptom and nonsymptom toxicities must be paired with prophylactic and therapeutic strategies. These strategies should be based on clinical practice guidelines that synthesize evidence and use multiprofessional, multidisciplinary expertise to place this evidence in clinical context and create recommendations. How best to implement clinical practice guidelines remains a challenge, but EHR order sets and alerts may be useful. In summary, although survival is excellent for pediatric patients receiving cancer therapies, more focus is needed on identification of symptoms and nonsymptom toxicities and their management. The EHR may be useful for promoting better supportive care through these mechanisms.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".