Comparing the Visual Analog Scale and the Numerical Rating Scale in Patient-reported Outcomes in Psoriatic Arthritis
Bibliographic record
Abstract
OBJECTIVE: Patient self-report scales are invaluable in psoriatic arthritis (PsA), as they allow physicians to rapidly assess patient perspectives of disease activity. We aimed to assess the agreement of the visual analog scale (VAS), a 100-mm horizontal line, and the numerical rating scale (NRS), a 21-point scale ranging from 0 to 10 in increments of 0.5, in patients with PsA. METHODS: Data were collected prospectively across 3 UK hospital trusts from 2018 to 2019. All patients completed the VAS and NRS for pain, arthritis, skin psoriasis (PsO), and global disease activity. A subset completed an identical pack 1 week later. Demographic and clinical data were also collected. Agreement was assessed using medians and the Bland-Altman method. Intraclass correlation coefficients (ICCs) were used to assess test-retest reliability. Spearman rank correlation coefficients were used to assess dependency between scale scores and clinical variables. RESULTS: Two hundred ten patients completed the study; 1 withdrew consent. Thus, 209 were analyzed. For pain, arthritis, skin PsO, and global disease activity, the difference between the VAS and NRS lay mostly within 1.96 SD of the mean, suggesting reasonable agreement between the 2 scales. Among the patients, 64.1% preferred the NRS. The ICCs demonstrated excellent test-retest reliability for both VAS and NRS. Higher VAS and NRS scores were associated with increased tender/swollen joint count, poorer functional status, and greater life impact. CONCLUSION: The VAS and NRS show reasonable agreement in key patient-reported outcomes in PsA. Results from both scales are correlated with disease severity and life impact.
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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 | 0.047 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".