Joint European Academy of Neurology–European Pain Federation–Neuropathic Pain Special Interest Group of the International Association for the Study of Pain guidelines on neuropathic pain assessment
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: In these guidelines, we aimed to develop evidence-based recommendations for the use of screening questionnaires and diagnostic tests in patients with neuropathic pain (NeP). METHODS: We systematically reviewed studies providing information on the sensitivity and specificity of screening questionnaires, and quantitative sensory testing, neurophysiology, skin biopsy, and corneal confocal microscopy. We also analysed how functional neuroimaging, peripheral nerve blocks, and genetic testing might provide useful information in diagnosing NeP. RESULTS: Of the screening questionnaires, Douleur Neuropathique en 4 Questions (DN4), I-DN4 (self-administered DN4), and Leeds Assessment of Neuropathic Symptoms and Signs (LANSS) received a strong recommendation, and S-LANSS (self-administered LANSS) and PainDETECT weak recommendations for their use in the diagnostic pathway for patients with possible NeP. We devised a strong recommendation for the use of skin biopsy and a weak recommendation for quantitative sensory testing and nociceptive evoked potentials in the NeP diagnosis. Trigeminal reflex testing received a strong recommendation in diagnosing secondary trigeminal neuralgia. Although many studies support the usefulness of corneal confocal microscopy in diagnosing peripheral neuropathy, no study specifically investigated the diagnostic accuracy of this technique in patients with NeP. Functional neuroimaging and peripheral nerve blocks are helpful in disclosing pathophysiology and/or predicting outcomes, but current literature does not support their use for diagnosing NeP. Genetic testing may be considered at specialist centres, in selected cases. CONCLUSIONS: These recommendations provide evidence-based clinical practice guidelines for NeP diagnosis. Due to the poor-to-moderate quality of evidence identified by this review, future large-scale, well-designed, multicentre studies assessing the accuracy of diagnostic tests for NeP are needed.
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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 | 0.027 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.008 |
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".