Three new datasets supporting use of the Numerical Rating Scale (NRS-11) for children’s self-reports of pain intensity
Bibliographic record
Abstract
Despite wide usage of the Numerical Rating Scale (NRS) for self-report of pain intensity in clinical practice with children and adolescents, validation data are lacking. We present here three datasets from studies in which the NRS was used together with another self-report scale. Study A compared post-operative pain ratings on the NRS with scores on the Faces Pain Scale-Revised (FPS-R) in 69 children age 7-17 years who had undergone a variety of surgical procedures. Study B compared post-operative pain ratings on the NRS with scores on the Visual Analogue Scale (VAS) in 29 children age 9-17 years who had undergone pectus excavatum repair. Study C compared ratings of remembered immunization pain in 236 children who comprised an NRS group and a sex- and age-matched VAS group. Correlations of the NRS with the FPS-R and VAS were r=0.87 and 0.89 in Studies A and B, respectively. In Study C, the distributions of scores on the NRS and VAS were very similar except that scores closest to the no pain anchor were more likely to be selected on the VAS than the NRS. The NRS can be considered functionally equivalent to the VAS and FPS-R except for very mild pain (<1/10). We conclude that use of the NRS is tentatively supported for clinical practice with children of 8years and older, and we recommend further research on the lower age limit and on standardized age-appropriate anchors and instructions for this scale.
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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 | 0.016 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".