A Systematic Review and Synthesis of Psychometric Properties of the Numeric Pain Rating Scale and the Visual Analog Scale for Use in People With Neck Pain
Bibliographic record
Abstract
OBJECTIVES: To conduct a systematic search and synthesis of evidence about the measurement properties of the Numeric Pain Rating Scale (NPRS) and the Visual Analog Scale (VAS) as patient-reported outcome measures in neck pain research. METHODS AND MATERIALS: CINAHL, Embase, PsychInfo, and MedLine databases were searched to identify studies evaluating the psychometric properties of the NPRS and the VAS used in samples of which >50% of participants were people with neck pain. Quality and consistency of findings were synthesized to arrive at recommendations. RESULTS: A total of 46 manuscripts were included. Syntheses indicated high-to-moderate-quality evidence of good-to-excellent (intraclass correlation coefficient 0.58 to 0.93) test-retest reliability over an interval of 7 hours to 4 weeks. Moderate evidence of a clinically important difference of 1.5 to 2.5 points was found, while minimum detectable change ranged from 2.6 to 4.1 points. Moderate evidence of a moderate association (r=0.48 to 0.54) between the NPRS or VAS and the Neck Disability Index. Findings from other patient-reported outcomes indicated stronger associations with ratings of physical function than emotional status. There is limited research addressing the extent that these measures reflect outcomes that are important to patients. DISCUSSION: It is clear NPRS and the VAS ratings are feasible to implement, provide reliable scores and relate to multi-item patient-reported outcome measures. Responsiveness (meaningful change) of the scales and interpretation of change scores requires further refinement. The NPRS can be a useful single-item assessment complimenting more comprehensive multi-item patient-reported outcome measures in neck pain research and practice.
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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 | 0.034 | 0.153 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.008 |
| Bibliometrics | 0.021 | 0.020 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".