Validation and properties of the verbal numeric scale in children with acute pain
Bibliographic record
Abstract
Although the verbal numeric scale (VNS) is used frequently at patients' bedsides, it has never been formally validated in children with acute pain. In order to validate this scale, a prospective cohort study was performed in children between 8 and 17years presenting to a pediatric emergency department (ED) with acute pain. Pain was graded using the VNS, the visual analogue scale (VAS), and the verbal rating scale (VRS). A second assessment was done before discharge. We determined a priori that in order to be valid, the VNS would need to: correlate with the VAS (concurrent validity); decrease after intervention to reduce pain (construct validity); and be associated with the VRS categories (content validity). The VNS interchangeability with the VAS, its minimal clinically significant difference, and test-retest reliability were also determined. A total of 202 patients (mean age: 12.2+/-2.6years) were enrolled. The VNS correlated with the VAS: r(ic)=0.93, p<0.001. There were differences in the VNS before versus after interventions (p<0.001), and between VRS categories (mild versus moderate, p<0.001; moderate versus severe, p<0.001). The 95% limits of agreement (interchangeability) between VNS/VAS were outside the a priori set limit of +/-2.0: -1.8, 2.5. The VNS minimal clinically significant difference was 1. The VNS had good test-retest reliability with 95% limits of agreement of -0.9 and 1.2. In conclusion, the VNS provides a valid and reliable scale to evaluate acute pain in children aged 8-17years but is not interchangeable with the VAS.
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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 | 0.022 | 0.069 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".