Diagnostic criteria for vasovagal syncope based on a quantitative history
Bibliographic record
Abstract
AIMS: Our goal was to develop historical criteria for the diagnosis of vasovagal syncope. METHODS AND RESULTS: We administered a 118-item historical questionnaire to 418 patients with syncope and no apparent structural heart disease. The prevalence of each item was compared between patients with positive tilt tests and those with syncope of other, known causes. The contributions of symptoms to diagnoses were estimated with logistic regression, point scores were developed, and the scores were tested using receiver operator characteristic analysis. The accuracy of the decision rule was assessed with bootstrapping. Data sets were complete for all subjects. The causes of syncope were known in 323 patients and included tilt-positive vasovagal syncope (235 patients) and other diagnoses such as complete heart block and supraventricular tachycardias (88 patients). The point score correctly classified 90% of patients, diagnosing vasovagal syncope with 89% sensitivity and 91% specificity. The decision rule suggested that 68% of an additional 95 patients with syncope of unknown cause and a negative tilt test have vasovagal syncope. CONCLUSION: A simple point score of historical features distinguishes vasovagal syncope from syncope of other causes with very high sensitivity and specificity.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".