Impact of atrial fibrillation on productivity in working-age patients: an analysis of Swiss-AF prospective cohort study data
Bibliographic record
Abstract
AIMS: We aimed to explore atrial fibrillation (AF)-induced productivity losses in working-age atrial fibrillation patients and to estimate atrial fibrillation-related indirect costs. METHODS: Between 2014 and 2017, the Swiss Atrial Fibrillation prospective cohort study (Swiss-AF) enrolled 217 working-age patients with documented atrial fibrillation. Self-reported changes in professional activity and the reasons thereof were descriptively analysed over 8 years of follow-up or until patients reached the retirement age. Results were put into perspective, and indirect costs were planned to be estimated, through comparison with a general population-based, age-, sex- and year-matched comparison sample from the Swiss labour force survey (SLFS). RESULTS: Of 217 analysed Swiss-AF patients, 14.7% reported a professional activity change (9.2% stop, 5.5% reduction) due to atrial fibrillation before the end of observation. Of those working at enrolment (n = 157), 3.8% had a subsequent professional activity change due to atrial fibrillation, 11.6% due to other reasons. Patients were more likely to report an impact of atrial fibrillation on professional activity if they had had atrial fibrillation longer and were closer to the retirement age. Slightly fewer Swiss-AF patients were employed (75%) than in the comparison sample (77%). For those working however, the degree of employment was higher (88% vs 83%). Lack of differences between the Swiss-AF patients and the comparison sample indicated no relevant indirect costs of atrial fibrillation due to lost productivity. CONCLUSION: Only a minority of atrial fibrillation patients reported a negative impact of atrial fibrillation on their professional activity. Professional activity changes due to other reasons were reported more frequently. Compared with the general population, atrial fibrillation did not cause distinct differences.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".