Arabic Translation and Validation of Olfactory-Specific Quality of Life Assessment Questionnaire
Bibliographic record
Abstract
BACKGROUND: Olfaction plays a critical role in our health, emotions, social life and safety, which is why olfactory dysfunction has a great impact on a person's life. This has been highlighted with the recent coronavirus disease 2019 (COVID-19) pandemic. Despite Arabic being the fifth most commonly spoken language and one of the six official languages of the United Nations, there is no Arabic version for an olfactory-specific quality of life assessment tool. METHOD: The Questionnaire of Olfactory Disorders-Negative Statements (QOD-NS) is a validated questionnaire that assesses many aspects of a patient's daily life. We translated this questionnaire to the Arabic language following European Organisation for Research and Treatment of Cancer (EORTC) Quality of Life Group Translation Procedure guidelines. A pilot-testing of the Arabic version was done among 20 participants, 10 of whom were confirmed to have normosmia based on scoring at least 11/12 on the Sniffin' Sticks (SS) olfactory testing (Group 1) and another 10 participants who reported anosmia and scored less than 7/12 on the SS test. Patients could agree, partially agree, partially disagree, or disagree with each questionnaire statement. RESULTS: The pilot study revealed that participants with confirmed anosmia had higher questionnaire scores compared to participants with normosomia (median 22 compared to 1, p value < 0.001). For each statement on the Arabic questionnaire, all questions scored at least 80% of intra-rater reliability, and the overall intra-rater reliability was 90%. CONCLUSION: The Arabic translation of QOD-NS is a validated questionnaire that can be used both in academic and clinical practice.
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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 | 0.004 | 0.011 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".