Characterizing Pain in Long-Term Survivors of Childhood Cancer
Bibliographic record
Abstract
Many long-term survivors of childhood cancer (LTSCCs) experience late- and long-term effects from their treatments, including pain. Yet, pain is poorly understood among LTSCCs. The current study aims to 1a) identify rates and patterns of chronic pain 1b) describe multiple dimensions of pain, and 2) test predictors of chronic pain in LTSCCs. Survivors [n=140; 48.6% male, Mage=17.3 years (SD=4.9)] were recruited from across Canada. Participants completed the Pain Questionnaire, Pain Catastrophizing Scale, Pediatric Quality of Life Inventory, Patient Reported Outcome Measurement Information System (PROMIS) – Pain Interference, Anxiety, and Depression scales, Child Posttraumatic Stress Scale, the Posttraumatic Stress Disorder Checklist for the DSM-V, and the Cancer Worry Scale. It was found that 26% of LTSCCs reported experiencing chronic pain. An exploratory cluster analysis revealed that 20% of survivors had a moderate to severe chronic pain problem based on measures of pain intensity and interference. The combination of anxiety, depression, PTSS, cancer worry, current age, age at diagnosis, pain catastrophizing, and sex significantly predicted the presence of chronic pain, χ2(8, N = 123) = 27.87, p < .001. Higher pain catastrophizing (OR = 1.09; 95% CI = 1.03-1.15) and older current age (OR = 1.13; 95% CI = 1.01-1.27) were significant predictors of chronic pain. LTSCCs should be screened for the presence and magnitude of chronic pain during their long-term follow-up visits so appropriate interventions can be discussed. Future research should investigate pain interventions tailored for this population.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".