Weighing In The Evidence: Lifestyle Modification In The Treatment Of Atrial Fibrillation
Bibliographic record
Abstract
Imagine if you suddenly felt your heart “jumping out of your chest” – this is the case for an estimated 1 in 4 Canadians who experience this rapid and chaotic heartbeat characteristic of atrial brillation (AF). The healthy heart works continuously to beat regularly under the control of electrical impulses originating from the sinoatrial (SA) node, the heart’s natural pacemaker. In AF, electrical impulses do not originate in the SA node, but rather, from a different part of the atrium or in nearby pulmonary veins. These abnormal electrical signals become rapid and disorganized, radiating throughout the atrial walls in an uncoordinated manner. This can cause the walls of the atrium to quiver, or brillate, which results in irregular electrical transmission from the atria to the ventricles. A normal heart rate at rest should be between 60-100 beats per minute at rest, but in AF, it can be considerably higher than 140 beats per minute1. Affecting more than 33 million individuals worldwide, AF is the most common sustained irregular heart rhythm encountered in clinical practice2. The progression and maintenance of AF results in adverse events, including an increase in hospitalizations and a ve-fold increase in the risk of stroke3. Given this evidence and anticipated increases in life expectancy within the next several decades, there are clear public health implications for the aging Canadian population.
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 | 0.003 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".