Validation of the Non-communicating Children's Pain Checklist–Postoperative Version
Bibliographic record
Abstract
BACKGROUND: This study evaluated the psychometric properties of the Non-communicating Children's Pain Checklist-Postoperative Version (NCCPC-PV) when used with children with severe intellectual disabilities. METHODS: The caregivers of 24 children with severe intellectual disabilities (aged 3-19 yr) took part. Each child was observed by one of their caregivers and one of the researchers for 10 min before and after surgery. They independently completed the NCCPC-PV and made a visual analog scale rating of the child's pain intensity for those times. A nurse also completed a visual analog scale for the same observations. RESULTS: The NCCPC-PV was internally reliable (Cronbach alpha = 0.91) and showed good interrater reliability. A repeated-measures analysis of variance indicated NCCPC-PV total and subscale scores were significantly higher after surgery and did not differ by observer. Postoperative NCCPC-PV scores correlated with visual analog scale ratings provided by caregivers and researchers, but not with those of nurses. A score of 11 on the NCCPC-PV, by caregivers, provided 0.88 sensitivity and 0.81 specificity for classifying children with moderate to severe pain. CONCLUSIONS: The NCCPC-PV displayed good psychometric properties when used for the postoperative pain of children with severe intellectual disabilities and has the potential to be useful in a clinical setting. The results suggest familiarity with an individual child with intellectual disabilities is not necessary for pain assessment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".