Biopsychosocial risk factors for pain in early phases of pediatric cancer treatment
Bibliographic record
Abstract
Introduction: Cancer pain remains a significant burden among children with cancer, and many patients experience pain starting around the time of diagnosis and throughout the course of treatment. A biopsychosocial treatment approach has been recommended to improve pain management in this population; however, specific psychosocial factors that contribute to pain in the early phases of pediatric cancer treatment have yet to be identified. The purpose of this study was to explore the biopsychosocial factors associated with pain experiences during the early phases of pediatric cancer treatment, with the goal of identifying children who may be at the highest risk for pain to inform future intervention and prevention efforts. Methods: = 10 weeks). Children completed self-report questionnaires and caregivers completed self- and parent-proxy questionnaires at baseline. Cancer-related data, including diagnosis and date of diagnosis, were abstracted from the child's electronic medical record. Multiple regression analysis was used to examine associations between biopsychosocial risk factors, pain intensity, and pain interference. Results: Older age, female gender, and elevated depressive symptoms, fatigue, and child self-reported pain catastrophizing were significantly associated with increased pain intensity. Additionally, lower annual income, decreased physical functioning, and greater fatigue, child pain catastrophizing, and parent stress were significantly associated with increased pain interference. Discussion: Findings underscore the importance of utilizing a comprehensive biopsychosocial approach to pain assessment and management in pediatric oncology. This approach highlights the need for targeted interventions that address not only the physical aspects of pain but also the psychological and social contexts of patients, ensuring a more holistic and effective treatment strategy.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".