Assessing Pain Intensity in Children with Chronic Pain: Convergent and Discriminant Validity of The 0 To 10 Numerical Rating Scale in Clinical Practice
Bibliographic record
Abstract
BACKGROUND: In clinical practice, children are often asked to rate their pain intensity on a simple 0 to 10 numerical rating scale (NRS). Although the NRS is a well-established measure for adults, no study has yet evaluated its validity for children with chronic pain. OBJECTIVES: To examine the convergent and discriminant validity of the NRS as it is used within regular clinical practice to document pain intensity for children with chronic pain. Interchangeability between the NRS and an analogue pain measure was also assessed. METHODS: A cohort of 143 children (mean [± SD] age 14.1±2.4 years; 72% female) rated their pain intensity (current, usual, lowest and strongest levels) on a verbally administered 0 to 10 NRS during their first appointment at a specialized pain clinic. In a separate session that occurred either immediately before or after their appointment, children also rated their pain using the validated 0 to 10 coloured analogue scale (CAS). RESULTS: NRS ratings met a priori criteria for convergent validity (r>0.3 to 0.5), correlating with CAS ratings at all four pain levels (r=0.58 to 0.68; all P<0.001). NRS for usual pain intensity differed significantly from an affective pain rating, as hypothesized (Z=2.84; P=0.005), demonstrating discriminant validity. The absolute differences between NRS and CAS pain scores were small (range 0.98±1.4 to 1.75±1.9); however, the two scales were not interchangeable. CONCLUSIONS: The present study provides preliminary evidence that the NRS is a valid measure for assessing pain intensity in children with chronic pain.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
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".