Spanish Translation, Cultural Adaptation, and Validation of the Standardized Cosmesis and Health Nasal Outcomes Survey Questionnaire
Bibliographic record
Abstract
Background: The Standardized Cosmesis and Health Nasal Outcomes Survey (SCHNOS) questionnaire is a new instrument that was developed to evaluate both functional and cosmetic components of rhinoplasty. It is a reliable, consistent, and validated patient-reported outcome measure that is not available in Spanish. Methods: The SCHNOS questionnaire was forward translated, back translated, and culturally adapted following international guidelines. Its psychometric validity was tested with native Spanish speakers in 2 centers in Colombia. The authors measured internal consistency, correlation, and reproducibility to determine validity of the instrument. Results: The final Spanish version of the SCHNOS was administered to 76 native Spanish speakers. Both the SCHNOS-O (obstructive domain) and SCHNOS-C (cosmetic domain) showed a high internal consistency with Cronbach’s alpha of 0.84 and 0.94, respectively. The Spearman correlations between the items of SCHNOS-O (0.38–0.82) and SCHNOS-C (0.49–0.88) were positive and significant. Spearman’s rank correlation in the test–retest analysis for SCHNOS-O (r = 0.87) and SCHNOS-C (r = 90) was positive and statistically significant. There was statistical significance in responses obtained for SCHNOS-O ( P < 0.001) but not for SCHNOS-C ( P = 0.222). Conclusions: In this study, the SCHNOS was successfully translated and culturally adapted into Spanish. The Spanish version of the SCHNOS was shown to be a reliable and valid instrument that we recommend it should be used in Spanish-speaking patients who are having functional or cosmetic rhinoplasty.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".