San Francisco Syncope Rule to predict short-term serious outcomes: a systematic review
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Bibliographic record
Abstract
BACKGROUND: The San Francisco Syncope Rule has been proposed as a clinical decision rule for risk stratification of patients presenting to the emergency department with syncope. It has been validated across various populations and settings. We undertook a systematic review of its accuracy in predicting short-term serious outcomes. METHODS: We identified studies by means of systematic searches in seven electronic databases from inception to January 2011. We extracted study data in duplicate and used a bivariate random-effects model to assess the predictive accuracy and test characteristics. RESULTS: We included 12 studies with a total of 5316 patients, of whom 596 (11%) experienced a serious outcome. The prevalence of serious outcomes across the studies varied between 5% and 26%. The pooled estimate of sensitivity of the San Francisco Syncope Rule was 0.87 (95% confidence interval [CI] 0.79-0.93), and the pooled estimate of specificity was 0.52 (95% CI 0.43-0.62). There was substantial between-study heterogeneity (resulting in a 95% prediction interval for sensitivity of 0.55-0.98). The probability of a serious outcome given a negative score with the San Francisco Syncope Rule was 5% or lower, and the probability was 2% or lower when the rule was applied only to patients for whom no cause of syncope was identified after initial evaluation in the emergency department. The most common cause of false-negative classification for a serious outcome was cardiac arrhythmia. INTERPRETATION: The San Francisco Syncope Rule should be applied only for patients in whom no cause of syncope is evident after initial evaluation in the emergency department. Consideration of all available electrocardiograms, as well as arrhythmia monitoring, should be included in application of the San Francisco Syncope Rule. Between-study heterogeneity was likely due to inconsistent classification of arrhythmia.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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 it