Validation and Reliability of the Neuropathic Pain Scale (NPS) in Multiple Sclerosis
Bibliographic record
Abstract
OBJECTIVE: Central neuropathic pain occurs in around 28% of patients with multiple sclerosis (MS). The Neuropathic Pain Scale (NPS) has received preliminary validation in peripheral neuropathic pain conditions. The aim of this study was to validate its use in MS central pain syndromes. METHODS: We administered the NPS to 141 patients with MS, together with the Short Form McGill Pain Questionnaire (SFMPQ), the Hospital Anxiety and Depression Scale (HADS), and Short Form 36 Health Survey (SF-36). RESULTS: Cronbach's alpha was 0.78 (95% CI 0.69; 0.83), implying a high degree of internal consistency. Three factors, "Familiar," "Superficial," and "Alien Perception," were extracted, accounting for 64% of the variance. The NPS 10-item total correlates with: the SFMPQ 15-item total score, rho=0.63 (95% CI 0.49; 0.74), its Visual Analog Scale, rho=0.49 (95% CI 0.33; 0.64), the transformed Pain domain of the SF-36 rho=-0.49 (95% CI -0.63; -0.32), but not with its remaining seven health domains, or with either the HADS anxiety or the depression scores. Limits of agreement for short-term test or re-test reliability of the 100 point NPS total (median 2 days, range 1 to 7) were -12 to 14 and when administered to 78 patients who rated their neuropathic pain the "Same" [median interval 33 days (range 19 to 126), the long-term test or re-test correlation coefficient was 0.71 (95% CI 0.6; 0.79)]. DISCUSSION: The NPS appears a useful tool in the assessment of neuropathic pain in MS patients and possibly in measuring outcomes of therapeutic interventions.
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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 | 0.017 | 0.038 |
| 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) | 0.001 | 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".