Syncope: how the EEG helps in understanding clinical findings
Bibliographic record
Abstract
Although it is now widely accepted that the key to accurate diagnosis and risk stratification of syncope is a thoughtful and scrupulous history, exactly what is meant by the history remains unclear, and moving it from experts to front-line workers has proven difficult. Partly this is because syncope is simply a symptom, like fever, with a plethora of potential causes. Partly as well this reflects the multitude of somewhat overlapping symptoms and signs for the most common form, the ‘faint’ or vasovagal syncope. There are several clinical features that are known to be helpful in the differential diagnosis of loss of consciousnesses, based on quantitative symptom studies. These for example help distinguish epileptic convulsions and pseudosyncope from syncope, but such studies were aimed at clinical decision-making. They reported just the most highly significant clinical points and do not help clinicians make sense of the welter of symptoms that clinical experience suggests (Sheldon et al. , 2002, 2006; Wieling et al. , 2009; Tannemaat et al. , 2013). In this issue of Brain , van Dijk et al. (2014) provide fascinating and informative insights into why some symptoms cluster with each other in patients with vasovagal syncope. The authors …
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".