The relation between children’s pain behaviour and developmental characteristics: a cross-sectional study
Bibliographic record
Abstract
AIM: To determine whether children with developmental disabilities show responses to pain that vary according to developmental level. METHOD: Factor analytical methods were used to explore whether pain behaviour is independent of developmental characteristics. As part of a longitudinal study, caregivers of 123 children (67 males, 56 females; age range 40 mo-21 y 6 mo) completed the Non-communicating Children's Pain Checklist-Revised (NCCPC-R), the Vineland Adaptive Behavior Scales, Second Edition (VABS-II), and the Pediatric Evaluation of Disability Inventory (PEDI). Deviation intelligence quotients (DIQs) were also generated. Two varimax rotated principal components analyses (PCAs) included the NCCPC-R subscales, DIQs, and age. One also included VABS-II subdomain scores and the other, PEDI scores, to allow examination of whether pain and developmental scores produced distinct components to evaluate the independence of pain behaviour from developmental factors. RESULTS: Children's mean age equivalents on the VABS-II were: Communication (36.4 mo, SD 34.8), Daily Living Skills (31.8 mo, SD 35.9), Socialization (43.2 mo, SD 49.9), and Motor Skills (21.6 mo, SD 20.3). Pain behaviour was distinct from developmental characteristics. The PCA including the VABS-II accounted for 78.4% of variance, with four components: Developmental Level, Pain Behaviour, Motor Development, and Chronological Age. The PCA that included the PEDI accounted for 69.4% of variance, with three corresponding components: Pain Behaviour, Developmental Level, and Chronological Age. INTERPRETATION: Pain behaviour was distinct from developmental factors in two separate analyses using two functional measures. Clinicians can be confident that pain assessment with the NCCPC-R is not affected by children's developmental level.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".