Cross‑Cultural Validation of the Chronic Rhinosinusitis Patient-Reported Outcomes (CRS-PRO) Questionnaire in Portuguese
Bibliographic record
Abstract
Introduction Chronic rhinosinusitis (CRS) presents with different clinical patterns with variable responses to treatment. Clear criteria for specifying disease severity and assessing symptom control are lacking in the current literature. We aimed to perform a cross-cultural adaptation of the chronic rhinosinusitis patient-reported outcomes (CRS-PRO), creating a Portuguese version to use as a routine questionnaire in the evaluation of patients with CRS. Methods The CRS-PRO questionnaire was translated according to the recommendations of the International Society for Pharmacoeconomics and Outcomes Research (ISPOR) through a three-step procedure including a backward translation. Results After translation completion, the questionnaire was evaluated in 40 participants (23 men) who completed the questionnaire on two separate occasions in 1.4 minutes (SD 0.615). Twenty of them were patients with CRS (60% with polyps), and the other 20 were healthy subjects who were considered a control group. The average age of the study participants was 43 years old (SD 16). The intraclass correlation coefficient (ICC) values for the CRS with nasal polyps (CRSwNP) group ranged from 0.65 to 0.89, indicating good to excellent reliability across the 12 items. All ICC values were statistically significant (p < 0.01). Conclusion This study presents the Portuguese version of the CRS-PRO questionnaire, an adapted, validated, and well-accepted instrument for evaluating CRS symptoms in the Portuguese-speaking population.
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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 | 0.010 | 0.015 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".