LINGUISTIC VALIDATION, PSYCHOMETRIC EVALUATION AND CROSS- CULTURAL ADAPTATION OF THE GEORGIAN SINO-NASAL OUTCOME TEST.
Bibliographic record
Abstract
PURPOSE: The objective of this prospective case-control study was to perform translation, cross-cultural adaptation, and validation of the Sino-nasal outcome test 22 (SNOT-22) into the Georgian language. METHODS: The translation and validation of the SNOT -22 questionnaire was performed using the forward-backward translation technique. After proper translation, the translated questionnaire was completed by chronic rhinosinusitis (CRS) patients before and after functional endoscopic sinus surgery (FESS) and by healthy individuals as controls. RESULTS: SNOT22 was translated into the Georgian language; the pilot study involved 34 patients, the test-retest group consisted of 30 patients with CRS and the control group of 71 patients without CRS complaints; 34 patients were evaluated before surgery and 3 months after surgery. The results showed a good internal correlation with Cronbach's alpha - 0.88 at the initial examination, and 0.93 at the retest examination; both values suggest good internal consistency within SNOT-22. Pearson's correlation coefficient was 0.72 (p<0.001), revealing a good correlation between initial scores and retest scores. Our sample of healthy individuals had a median score of 10,11 points and the instrument was able to differentiate between the healthy and the patient group, demonstrating its validity (p<0.0001). CONCLUSIONS: The Georgian version of the SNOT-22 questionnaire is a valid outcome measure for patients with CRS.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".