Cross-Cultural Adaptation of the CRS-PRO Questionnaire into French
Bibliographic record
Abstract
BACKGROUND: Chronic rhinosinusitis (CRS), encompasses many different clinical patterns with variable response to treatment. Precise criteria specifying disease severity and control are lacking in the current literature. Our aim was to perform a cross-cultural adaptation of the CRS-PRO, creating a French version for use as a routine questionnaire in the assessment 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: Seven of 12 items were initially discordant between the three translators before achieving consensus (Step 1). Two of 12 items were discordant between the backward translation and the initial CRS-PRO version regarding the word "mucus"(Step 2). Step 3 allowed the creation of a French proof-read version of the CRS-PRO questionnaire. Thirty patients were included for initial validation, mean age of 49.2 ± 15 years and 63.3% (19/30) male. It took them 67 ± 23 s to complete the questionnaire without any patients requiring more than 2 min. CONCLUSION: This study presents the French version of the CRS-PRO questionnaire-an adapted, validated, and well-accepted instrument to evaluate the CRS symptoms in the French 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 | 0.007 | 0.010 |
| 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 | 0.001 |
| 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".