Cerebral palsy pain instruments: Recommended tools for clinical research studies by the National Institute of Neurological Disorders and Stroke Cerebral Palsy Common Data Elements project
Bibliographic record
Abstract
AIM: This study describes the process of updating the cerebral palsy (CP) common data elements (CDEs), specifically identifying tools that capture the impact of chronic pain on children's functioning. METHOD: Through a partnership between the American Academy for Cerebral Palsy and Developmental Medicine and the National Institute of Neurological Disorders and Stroke (NINDS), the CP CDEs were developed as data standards for clinical research in neuroscience. Chronic pain was underrepresented in the NINDS CP CDEs version 1.0. A multi-step methodology was applied by an interdisciplinary professional team. Following an adapted CP chronic pain tools' rating system, and a review of psychometric properties, clinical utility, and compliance with inclusion/exclusion criteria, a set of recommended pain tools was posted online for external public comment in May 2022. RESULTS: Fifteen chronic pain tools met inclusion criteria, representing constructs across all components of the International Classification of Functioning, Disability and Health. INTERPRETATION: This paper describes the first condition-specific pain CDEs for a pediatric population. The proposed set of chronic pain tools complement and enhance the applicability of the existing pediatric CP CDEs. The novel CP CDE pain tools harmonize the assessment of chronic pain, addressing not only intensity of chronic pain, but also the functional impact of experiencing it in everyday activities.
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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 | 0.178 | 0.242 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.018 | 0.026 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".