Syncope: Review of Monitoring Modalities
Bibliographic record
Abstract
Elucidating the underlying cause of unexplained syncope, palpitations or other possible arrhythmia-related symptoms is a formidable clinical challenge. Cardiac monitoring supplements the most important "test" in patients with syncope or palpitations, that of a thoughtful history and physical examination. Ideally, comprehensive physiologic monitoring during spontaneous symptoms would constitute what, at present, is an unattainable gold standard test for establishing a cause. Short of that goal, establishing an accurate symptom-rhythm correlation can often provide a diagnosis. Ambulatory outpatient monitoring is a powerful diagnostic tool for the evaluation of cardiac arrhythmias. Evolving technologies have provided a vast array of monitoring options for patients suspected of having cardiac arrhythmias, with each modality differing in duration of monitoring, quality of recording, convenience and invasiveness. Holter monitors, event monitors and external loop recorders are non-invasive and provide easily accessible short-term monitoring solutions. In instances where the diagnosis remains elusive, a more long-term strategy with an implantable loop recorder may be the preferred path.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".