Bibliographic record
Abstract
This editorial refers to ‘Detection of atrial high-rate events by continuous Home Monitoring: clinical significance in the heart failure–cardiac resynchronization therapy population’ by N. Shanmugam et al. , on page 230 Depending on one's perspective, modern pacemakers and defibrillators provide physicians with a wealth or burden of diagnostic information. Currently, there is great interest in the ability of these devices to monitor thoracic impedance, ST-segments, and other physiological parameters.1,2 However, the ability to document and characterize otherwise undetected atrial tachyarrhythmias, referred to as atrial high-rate episodes (AHRE), has been available for >10 years.3 Despite this familiarity with AHRE, there remains a formidable knowledge gap which stands between physicians and the optimal use of these data for patient care. As paroxysmal atrial fibrillation appears to pose the same risk of stroke as sustained episodes,4 it is tempting to simply assume that AHRE should be managed like conventionally diagnosed atrial fibrillation. However, these otherwise silent atrial tachyarrhythmias may have a significantly different prognosis and the impact of prophylactic oral anticoagulation may be quite different in this population. The ASSERT (A Symptomatic Stroke and atrial fibrillation Evaluation …
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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; both teacher heads agree on what is shown here.
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".