Pitcher perfect: arrhythmia monitoring at the Munich Oktoberfest
Bibliographic record
Abstract
This editorial refers to ‘Alcohol consumption, sinus tachycardia, and cardiac arrhythmias at the Munich Octoberfest: results from the Munich Beer Related Electrocardiogram Workup Study (MunichBREW)’†, by S. Brunner et al., on page 2100. Atrial fibrillation (AF) is the most common cardiac arrhythmia worldwide and an important cause of morbidity and mortality.1 A better understanding of the risk factors predisposing to AF and its underlying mechanisms are thus of major public health importance as they may potentially lead to new preventive strategies. Alcohol consumption is known to have profound cardiac effects, both beneficial and deleterious. For example, low to moderate alcohol consumption has been linked to a favourable, inverse relationship with coronary artery disease,2 while a U-shaped relationship has been described with congestive heart failure3 and sudden cardiac death.4 With regards to regular alcohol consumption and new-onset AF, earlier studies suggested a threshold effect.5 However, more recent meta-analyses suggest a linear increase in AF risk across the entire spectrum of alcohol intake. In a recent meta-analysis of prospective studies, each additional unit of alcohol intake was associated with an 8% increased risk of AF.6
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.026 | 0.019 |
| Insufficient payload (model declined to judge) | 0.016 | 0.008 |
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".