Psychometric Properties of the Numerical Rating Scale to Assess Self-Reported Pain Intensity in Children and Adolescents
Bibliographic record
Abstract
OBJECTIVES: The Numerical Rating Scale-11 (NRS-11) is one of the most widely used scales to assess self-reported pain intensity in children, despite the limited information on its psychometric properties for assessing pain in pediatric populations. Recently, there has been an increase in published findings regarding the strengths and weaknesses of the NRS-11 as a measure of pain in youths. The purpose of this study was to review this research and summarize what is known regarding the reliability and validity of the NRS-11 as a self-report measure of pediatric pain intensity. METHODS: A literature search was conducted using PubMed, PsycINFO, CINAHL, and the Psychology and Behavioral Sciences Collection from their inception to February 2016. RESULTS: A total of 382 articles were retrieved, 301 were screened for evaluation, and 16 were included in the review. The findings of reviewed studies support the reliability and validity of the NRS-11 when used with children and adolescents. DISCUSSION: Additional research is needed to clarify some unresolved questions and issues, including (1) the minimum age that children should have to offer valid scores of pain intensity and (2) the development of consensus regarding administration instructions, in particular with respect to the descriptors used for the upper anchor. On the basis of available information, the NRS-11 can be considered to be a well-established measure for use with pediatric populations.
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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 | 0.012 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".