Application of five risk stratification tools for syncope in older adults
Bibliographic record
Abstract
OBJECTIVE: Treatment of syncope in older adults places a burden on healthcare systems. We used five risk stratification tools to predict short-term adverse outcomes in older patients with syncope. METHODS: This was a retrospective analysis of patients with syncope (age ≥60 years) in the emergency department of an urban academic hospital. The data were evaluated using the Risk Stratification of Syncope in the Emergency Department (ROSE), San Francisco Syncope Rule (SFSR), FAINT, Canadian Syncope Risk Score (CSRS), and Boston Syncope Criteria (BSC) tools. Sensitivity, specificity, accuracy, positive and negative predictive value (NPV), and positive and negative likelihood ratios of each tool were calculated and compared for adverse events within 1 month. RESULTS: In total, 221 patients (average age 75.6 years) were analyzed. Fifty-nine patients (26.7%) had experienced an adverse event within 1 month. For the ROSE, SFSR, FAINT, CSRS and BSC tools, sensitivities were 81.3%, 76.3%, 93.2%, 71.2%, and 94.9%, specificities were 88.3%, 87.7%, 56.8%, 71.6%, and 67.3%, and NPVs were 92.9%, 91.0%, 95.8%, 87.2%, and 97.3%, respectively. CONCLUSION: The five assessed tools could be useful for physicians in screening older patients with syncope for the risk of short-term adverse events, according to the patient's actual situation.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".