Validation of the Arabic Chronic Rhinosinusitis Patient-Reported Outcome (CRS-PRO): Translation and Cultural Adaptation
Bibliographic record
Abstract
Background: The chronic rhinosinusitis patient-reported outcome (CRS-PRO) is a newly developed, disease-specific questionnaire designed for patients with CRS. This study focused on translating the CRS-PRO into Arabic, conducting cross-cultural adaptation and validation of the questionnaire, and assessing its reliability and validity. Methods: This prospective study involved 112 patients divided into CRS, functional endoscopic sinus surgery (FESS), and control groups. Participants completed the questionnaire at enrollment and again after one month. The Arabic version of the CRS-PRO was created following the International Society for Pharmacoeconomics and Outcomes Research guidelines for translation and cross-cultural adaptation. Results: This study included 74 males (66.1%) and 38 females (33.9%), with an average age of 37.4 ± 14.8 years. The Arabic CRS-PRO questionnaire has high internal consistency and reliability (Cronbach’s alpha 0.97). It also has strong discriminant validity in distinguishing between groups (ANOVA, p < 0.001). The assessment of test/retest symptom scores and their consistency over time confirmed the reliability of the CRS-PRO in differentiating CRS patients from healthy individuals and in monitoring surgical outcomes. This was validated through Pearson’s correlation coefficients (p < 0.01) and intraclass correlation (p < 0.0001). Conclusions: The Arabic version of the CRS-PRO proved simple, reliable, and valid. It showed high internal consistency, reliability, and strong discriminant validity in distinguishing between healthy individuals, CRS patients, and those pre- and post-FESS.
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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 | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".