Validity and reliability studies of the Indonesian version of Atrial Fibrillation Severity Scale (AFSS)
Bibliographic record
Abstract
BACKGROUND: In the atrial fibrillation (AF) population, worsened quality of life (QOL) has been reported even before complications occur. Symptom-based questionnaires can be used to evaluate AF treatment. The Atrial Fibrillation Severity Scale (AFSS) was first developed in Canada in English, which is not the main language in Indonesia. This study aims to test the reliability and validity of the Indonesian version of the Atrial Fibrillation Severity Scale (AFSS). METHODS: Translation of the AFSS from English to Indonesian was done using forward and backward translation. The final version was then validated with the Short Form-36 (SF-36) questionnaire, and a test-retest reliability study was done in a 7-14-day interval. RESULTS: An Indonesian version of AFSS was achieved and deemed acceptable by a panel of researchers. This version is reliable and valid, with Cronbach's α of 0.819, Intraclass Correlation Coefficient (ICC) ranging from 0.803 to 0.975, and total score correlation ranging from 0.333 to 0.895. Pearson's analysis of AFSS and SF-36 revealed that the total AF burden domain was poorly correlated with role limitations due to emotional problems (r:0.427; p < 0.01) and pain (r:0.495; p < 0.01). The symptom severity domain was poorly correlated with physical functioning (r:-0.335; p < 0.01), role limitations due to emotional problems (r:0.499; p < 0.01), pain (r:0.458; p < 0.01), and total SF-36 score (r:-0.361; p < 0.01). Total AFSS score was moderately correlated with role limitations due to emotional problems (r:0.516; p < 0.01) and pain (r:0.538; p < 0.01). The total AFSS score was poorly correlated with the European Heart Rhythm Association (EHRA) score (r:0.315; p < 0.01). CONCLUSION: The Indonesian version of AFSS has good internal and external validity with good reliability.
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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".