Validation of a New Arabic Version of the Neuropathic Pain Diagnostic Questionnaire (DN4)
Bibliographic record
Abstract
The "Douleur Neuropathique 4 (DN4) questionnaire" was developed for screening neuropathic pain. The purpose of this work was to validate the DN4 questionnaire in the standard Arabic language. First, the questionnaire was translated and semantically adapted to Arabic according to the international guidelines for cross-cultural adaptation. Second, a prospective observational study was performed to validate this questionnaire. A total of 195 patients with chronic pain (n = 99 with neuropathic pain and n = 96 without neuropathic pain) were enrolled in the study. The internal consistency Kuder-Richardson's Formula 20 for the whole DN4 questionnaire was 0.86 (P < 0.001) and the intraclass correlation coefficient 0.99 (95% CI: 0.99 to 1.00). The test-retest reliability kappa coefficient for each item ranged from 0.92 to 1.00. Using a receiver-operating characteristic (ROC) curve analysis, the areas under the curve were 0.94 and 0.97 for the 7-item DN4 and 10-item DN4, respectively. A cut-off score of 3 resulted in a sensitivity of 97.0% and a specificity of 82.3% for the 7-item DN4, while a cut-off score of 5 for the 10-item DN4 resulted in a sensitivity of 93.0% and a specificity of 95.8%. Tingling, numbness, and hypoesthesia to touch and to pricking were the most discriminating pain items. The sensitivity and specificity of the 7-item DN4 and 10-item DN4 were not influenced by either pain severity or educational level. In conclusion, this new Arabic version DN4 questionnaire is a simple, reliable, and valid tool for discriminating between neuropathic and non-neuropathic pain. It represents a useful tool in clinical setting and population-based studies.
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 | 0.006 | 0.014 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".