Atrial fibrillation is associated with increased mortality: causation or association?
Bibliographic record
Abstract
This editorial refers to ‘All-cause mortality in 272 186 patients hospitalized with incident atrial fibrillation 1995–2008: a Swedish nationwide long-term case–control study’†, by T. Andersson et al., on page 1061 It is important to determine whether the excess mortality observed in patients with atrial fibrillation (AF) is directly due to AF or is just an association. Not only do patients want to know if AF is a cause of premature death, but knowing that there is or is not a causal relationship will influence therapeutic choices. Such knowledge will also certainly impact on the AF research agenda for the future. If AF directly causes excess mortality, then the use of therapies that specifically and successfully eliminate AF—rather than just prevent its symptoms—are preferable. Andersson et al. have now presented their findings from a retrospective observational study of patients with incident AF compared with AF-free controls identified through national databases.1 Similar to previous studies (Table 1), they report that AF is an independent risk factor for all-cause mortality. Although the study by Andersson et al. was very carefully conducted and is one of the largest ever performed in patients with AF, can we conclude from these observational data that AF is a direct cause of premature death?
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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 | 0.007 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.020 | 0.016 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".