Pain Reported by Chinese Children During Cancer Treatment
Bibliographic record
Abstract
BACKGROUND: Pain is a frequently reported and distressing symptoms during cancer treatment. However, there is limited evidence on pain reported by Chinese children with cancer. OBJECTIVES: This study aimed to investigate the prevalence, intensity, interference, and management of pain reported by Chinese children during cancer treatment and explore the predictors of pain interference. METHODS: We conducted a cross-sectional survey to investigate the pain intensity, pain interference, co-occurring symptoms (anger, anxiety, depression, fatigue), and pain management strategies reported by children 8 years and older undergoing active cancer treatment in 4 Chinese hospitals. RESULTS: Data were analyzed for 187 children. The prevalence of moderate to severe pain (≥4/10) was 38.50%, with an average pain interference score of 52.97 out of 100. Approximately 24% of children were prescribed pain medicine. Pain interference and pain intensity were marginally correlated (r = 0.047, P < .01) and were both positively correlated with pain duration and co-occurring symptoms and negatively correlated with perceived pain alleviation (all P < .01). Multiple regression analyses suggested that severe pain intensity (B = 2.028, P = .003) and fatigue (B = 0.440, P < .001) significantly predicted higher levels of pain interference (R2 = 0.547, F = 23.102, P < .001). CONCLUSION: Chinese children with cancer reported a low pain intensity score but a relatively high level of pain interference. According to the children's reports, pain has not been sufficiently addressed through Chinese pediatric oncology supportive care. IMPLICATIONS FOR PRACTICE: There is an urgent requirement for comprehensive pain assessment and standardized, targeted interventions in Chinese pediatric oncology pain management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".