Psychometric Properties of the Persian Version of the ID-Pain Questionnaire.
Bibliographic record
Abstract
Background: The Identification Pain questionnaire (IDPQ) is one of the recommended tools by the Neuropathic Pain Special Interest Group of the International Association for the Study of Pain for neuropathic pain screening. This study aimed to translate, cross-culturally adapt, and validate the Persian version of the IDPQ. Methods: First, the IDPQ was translated based on the recommended guidelines. Afterward, the internal consistency (Cronbach's alpha coefficient), test-retest reliability (intraclass correlation coefficient), construct validity (compared to the Douleur Neuropathique 4 [DN4] questionnaire), and discriminant validity (Receiver operating curve analysis) of the IDPQ-P were evaluated. A total of 90 patients with neuropathic (n=50) and nociceptive pain (n=40) were enrolled in the study. In the next 72 h after the initial assessment, 30 patients (15 with neuropathic and 15 with nociceptive pain) completed the IDPQ-P. Results: No modifications were needed in the process of translation and cultural adaptation. Cronbach's alpha coefficient was obtained at 0.47 for all patients, indicating poor internal consistency. The intraclass correlation coefficient was estimated at 0.97, showing excellent test-retest reliability. A high correlation was found between the DN4 questionnaire and IDPQ-P (0.74), showing acceptable construct validity. The area under the curve was 0.94 (95% CI: 0.88-0.99) and 0.92 (95% CI: 0.85-0.99) when the physician's diagnosis and the DN4 cut-off value were used as the reference standard, respectively. The optimal cut-off value of ≥ 2 demonstrated the highest sensitivity (98%) and specificity (79%). Conclusion: The IDPQ-P can be used in the clinical setting as an accurate and quick screening tool to diagnose patients with neuropathic pain. Sufficient test-retest reliability, construct validity, discriminant validity, and high diagnostic accuracy were found for the IDPQ-P.
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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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".