The sensitivity and specificity of using the McGill pain subscale for diagnosing neuropathic and non-neuropathic chronic pain in the total joint arthroplasty population
Bibliographic record
Abstract
BACKGROUND: The purpose of this study was to describe the diagnostic performance of the Neuropathic Pain Subscale of McGill [NP-MPQ (SF-2)] and the Self-Administered Leeds Assessment of Neuropathic Symptoms and Signs (S-LANSS) questionnaire in differentiating people with neuropathic chronic pain post total joint arthroplasty (TJA). METHODS: This study was a survey of a cohort of individuals who had undergone primary, unilateral total knee, or hip joint arthroplasty. The questionnaires were administered by mail. The time interval from operation to the completion of the postal survey varied from 1.5 to 3.5 years post-surgery. Receiver Operating Characteristic (ROC) analysis was used to assess the overall diagnostic power and determine the optimal threshold value of the NP-MPQ (SF-2) in identification of neuropathic pain. RESULTS: S-LANSS identified 19 subjects (28%) as having neuropathic pain (NP), while NP-MPQ (SF-2) subscale identified 29 (43%). When using the S-LANSS as the reference standard, a Receiver Operating Characteristic (ROC) analysis for NP-MPQ (SF-2) had an area under the curve of 0.89 (95% CI: 0.82, 0.97); a cut off score of 0.91 NP-MPQ (SF-2) maximized sensitivity (89.5%) and specificity (75.0%). Correlation between the measures was moderate (r = 0.56; 95% CI: 0.40, 0.68). CONCLUSION: These finding suggest some conceptual overlap but some variability in diagnosis of NP which may relate to scale-tapping into different dimensions of the pain experience, or the different scoring metrics.
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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 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.000 |
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".