Psychometric properties of the Neuropathic Pain Scale (NPS) in a knee osteoarthritis population
Bibliographic record
Abstract
Objective: Symptoms resembling neuropathic pain (neuropathic-like symptoms) are prevalent in osteoarthritis (OA) populations. Scales that measure neuropathic-like symptoms frequently were established in groups with true neuropathic pain conditions and have not been assessed in OA. We assessed the psychometric properties of the Neuropathic Pain Scale (NPS) in subjects with OA undergoing total knee replacement (TKR). Design: In a prospective study of adults undergoing TKR for OA, we assessed baseline distributions, acceptability (completion rate), internal consistency (Cronbach's alpha), responsiveness 12 months post-TKR, and construct validity of the NPS. We performed factor analysis and created subscales from the items loading onto each retained factor. We evaluated subscale properties and calculated the proportion of total scores attributable to each subscale and compared this with the proportion expected if each item contributed equally. Results: Mean baseline NPS score among 263 participants was 42.7 (SD: 15.9). Cronbach's alpha was 0.88. Factor analysis produced two factors: "bothersome" (items: intense/sharp/dull/unpleasant/deep; Cronbach's alpha = 0.87), and "dysesthetic" (items: cold/sensitive/itchy/surface; alpha = 0.77). Bothersome items contributed more to total NPS scores (74%) than would be expected if each item contributed equally (50%). NPS scores correlated moderately with baseline pain and function, and decreased after TKR, with standardized response means (SRMs) of: total NPS: 1.77, Bothersome subscale: 2.03, Dysesthetic subscale: 0.70. Conclusions: The NPS had acceptable completion, internal consistency, and construct validity, but was not optimal for use in OA; Bothersome subscale items disproportionately drive total NPS scores and may fail to discriminate between nociceptive and neuropathic-like symptoms.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".