Chronic Pain Assessment Tools for Cerebral Palsy: A Systematic Review
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Chronic pain in children with cerebral palsy (CP) is underrecognized, leading to detriments in their physical, social, and mental well-being. Our objective was to identify, describe, and critique pediatric chronic pain assessment tools and make recommendations for clinical use for children with CP. Secondly, develop an evidence-informed toolbox to support clinicians in the assessment of chronic pain in children with disabilities. METHODS: Ovid Medline, Cumulative Index to Nursing and Allied Health Literature, and Embase databases were systematically searched by using key terms "chronic pain" and "clinical assessment tool" between January 2012 and July 2014. Tools from multiple pediatric health conditions were explored contingent on inclusion criteria: (1) children 1 to 18 years; (2) assessment focus on chronic pain; (3) psychometric properties reported; (4) written in English between 1980 and 2014. Pediatric chronic pain assessment tools were extracted and corresponding validation articles were sought for review. Detailed tool descriptions were composed and each tool underwent a formal critique of psychometric properties and clinical utility. RESULTS: Of the retrieved 2652 articles, 250 articles met eligibility, from which 52 chronic pain assessment tools were retrieved. A consensus among interprofessional working group members determined 7 chronic pain interference tools to be of importance. Not all tools have been validated with children with CP nor is there 1 tool to meet the needs of all children experiencing chronic pain. CONCLUSIONS: This study has systematically reviewed and recommended, through expert consensus, valid and reliable chronic pain interference assessment tools for children with disabilities.
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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 | 0.012 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".