Reliability and validity study of Sino-nasal outcome test 22 (Thai version) in chronic rhinosinusitis
Bibliographic record
Abstract
BACKGROUND: Chronic rhinosinusitis (CRS) is one of common health conditions that affects patients' health-related quality of life. Our purpose is to assess the reliability and validity of Thai-version of Sino-Nasal Outcome Test 22 in chronic rhinosinusitis. METHODS: Permission for translation of SNOT-22 from English language to Thai language was obtained from the developer. The translation process was done based on the international standard of translation method. A total of 80 subjects were recruited into the study and divided into two groups comprising of 50 patients with chronic rhinosinusitis and 30 healthy volunteers. Cronbach's α and Intraclass correlation coefficient were evaluated for its reliability. Validity test was evaluated against VAS score, SF-36 (Thai version) questionnaire and CT scan (based on Lund-Mackay score). Responsiveness was assessed between pre-operative and post-operative scores in 34 patients. RESULTS: < 0.05). In addition, the SNOT-22 Thai version also showed good responsiveness when compared between pre-operative and post-operative scores (p < 0.001) and also well-performed in effect size calculation (1.37). CONCLUSION: We demonstrated that Thai -version of SNOT-22 has good reliability and validity, suitable for evaluation of chronic rhinosinusitis symptoms together with severity of the disease and response to treatment. TRIAL REGISTRATION: Thai clinical trials registry TCTR20170320003. Date of registration 20/03/2017 (retrospectively registered).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".