Application of the American College of Emergency Physicians (ACEP) Recommendations and a Risk Stratification System (OESIL) for Patients with Syncope Admitted in Internal Medicine Service
Bibliographic record
Abstract
Syncope represents 1-6% of total admissions to the hospital. Identification of high-risk patients (pts) at the ER is essential for avoiding unnecessary admissions. To retrospectively evaluate the ACEP recommendations for syncope admissions and the OESIL risk score for syncope stratification in pts with syncope admitted to the Internal Medicine Service. Two blinded investigators review all the charts and retrospectively applied the guidelines. ACEP recommendations were divided into level B (high sensitivity and specificity to detect cardiac syncope) and C (high sensitivity, low specificity). OESIL risk score was divided into 0-1 points (less than 1% of mortality risk) and 2-4 (more than 20% of mortality risk). We assumed that pts with an OESIL > 2 should be admitted due to high risk of cardiac mortality. Between June 2003-July 2004, 75 pts were admitted. Mean age was 68±14 years, 41% were female. Structural heart disease was present in 60% and ECG was abnormal in 25%. A diagnosis was achieved in 40 pts (54%), vasovagal syncope 22 (55%), cardiac 6 (15%), orthostatic hypotension 7 (18%), drug-induced 2 (5%) and neurologic 2 (5%). The average length of stay was 4.2±3.7 days. ACEP level B Sensitivity 100%, Specificity 81%, ACEP level C; Sensitivity 100%, Specificity 26%. The majority of syncope admissions to an Internal Medicine Service were low risk. ACEP level B recommendations had a good sensitivity identifying cardiac causes of syncope. However, ACEP guidelines overestimate cardiac causes leading to unnecessary admissions. Level C recommendations have poor specificity (26%) leading to unnecessary admissions. OESIL score identified 30% of pts with very low mortality that may have been unnecessarily admitted.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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".