Global and Specific Behavioral Measures of Pain in Children With Cerebral Palsy
Bibliographic record
Abstract
OBJECTIVES: The aim of this research was to validate global and behavioral observation methods for measuring pain in children with cerebral palsy (CP). MATERIALS AND METHODS: Nineteen children diagnosed with CP (2-21 years of age) and their primary caregivers participated in this study. Children and their caregivers were videotaped in their home before, during, and after a stretching exercise, and tests of cognitive and social development were administered. Children who were able to pass a training task were also asked to rate their experience of pain using a numerical rating scale (self-report NRS), but only 5 children (24%) passed so their self-report scores were not included. Healthcare professionals rated videotaped segments for each of the 3 time periods in a randomized order using an observer NRS and the Non-Communicating Children's Pain Checklist-Postoperative Version (NCCPC-PV). Raters trained in the Child Facial Coding System (CFCS) examined the same videotaped segments. RESULTS: Results showed significantly greater pain behavior (observer NRS, NCCP- PV) during the stretching procedure than during the baseline and recovery segments. There were no significant differences in CFCS scores, across time segments. CONCLUSIONS: These findings support the hypothesis that children with CP express discernible pain behaviors regardless of cognitive or language ability. These results contribute to multidimensional assessment of pain in children with neurologic impairment.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".