The validity and reliability of the Turkish version of the University of Toronto Atrial Fibrillation Severity Scale
Bibliographic record
Abstract
BACKGROUND/AIM: There are various instruments to assess quality of life (QoL) in patients with atrial fibrillation (AF). The aim of this study is to determine the reliability and validity of the Turkish version of the University of Toronto Atrial Fibrillation Severity Scale (AFSS). MATERIALS AND METHODS: The AFSS and Short Form-36 (SF-36) were completed by 130 patients with documented AF. The Canadian Cardiovascular Society Severity in Atrial Fibrillation (SAF) scale and European Heart Rhythm Association (EHRA) scale were also utilized by the attending physicians. To assess test-retest reliability, the AFSS was readministered to 47 clinically stable patients at a 1-month follow-up visit. Internal consistency reliability, test-retest reproducibility, and construct validity were evaluated. RESULTS: The mean age of the patients was 63.1 + 10.9 years and 58.5% of patients were male. The outcome scores of the Turkish version of the AFSS showed good correlations with theoretically related SF-36 domains. Additionally, AFSS outcome scores showed a linear correlation with the SAF and EHRA scores. Cronbach's alpha values for internal consistency were consistent and similar with the English language version of the AFSS. Intraclass correlation coefficients for reproducibility exceeded 0.80 for every item. CONCLUSION: Convergent-divergent and known-groups validity and reliability were established for the Turkish version of the University of Toronto AFSS.
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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 | 0.003 | 0.012 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".