Validation of DN4 as a screening tool for neuropathic pain in painful diabetic polyneuropathy
Bibliographic record
Abstract
AIMS: DN4 (Douleur Neuropathique en 4 Questions) is a screening tool for neuropathic pain consisting of interview questions (DN4-interview) and physical tests. It has not formally been validated in diabetes. We evaluated the validity and diagnostic accuracy of DN4 and DN4-interview in identifying neuropathic pain of painful diabetic polyneuropathy. METHODS: In 158 patients with diabetes, the presence of diabetic polyneuropathy and neuropathic pain was assessed using scoring system for symptoms and signs, quantitative sensory testing, nerve conduction studies, pain history, numerical rating scale, and Short-Form McGill Pain Questionnaire. Painful diabetic polyneuropathy was defined as the presence of diabetic polyneuropathy plus chronic neuropathic pain in the same area as neuropathic deficits. A blinded investigator performed DN4. RESULTS: The DN4 score was significantly related to all the neurological and electrophysiological measurements and to Short-Form McGill Pain Questionnaire (ρ = 0.58, P < 0.0001). DN4 and DN4-interview scores showed a high diagnostic accuracy for painful diabetic polyneuropathy with areas under the receiver operating characteristic curve of 0.94 and 0.93, respectively. At the cut-off of 4, DN4 displayed sensitivity of 80%, specificity of 92%, positive predictive value (PPV) of 82%, negative predictive value (NPV) of 91%, and likelihood ratio for a positive result (LR(+) ) of 9.6. At the cut-off of 3, DN4-interview showed sensitivity and specificity of 84%, PPV of 71%, NPV of 92%, and LR(+) of 5.3. CONCLUSIONS: This is the first validation study of DN4 for painful diabetic polyneuropathy, which supports its usefulness as both a screening tool for neuropathic pain in diabetes and a reliable component of the diagnostic work up for painful diabetic polyneuropathy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".