Comparison of different risk stratification rules to predict short-term adverse outcomes after syncope in older Chinese adults
Bibliographic record
Abstract
Abstract Background: Older adults with syncope are commonly treated in the emergency department. Clinical decision rules have been developed to assess syncope patients, but there have been no application or comparative studies in older Chinese cohorts until now. This study aimed to compare the values of five existing rules in predicting the short-term adverse outcomes of older patients. Methods: From September 2018 to February 2021, older Chinese patients (≥60 yr) with syncope admitted to our hospital were investigated and evaluated by the Risk Stratification of Syncope in the Emergency Department (ROSE) rule, the San Francisco Syncope Rule (SFSR), the FAINT rule, the Canadian Syncope Risk Score (CSRS) and the Boston Syncope Criteria (BSC). After a one-month follow-up, the sensitivity, specificity, accuracy, positive predictive values (PPV), negative predictive values (NPV), positive likelihood ratios (PLR), and negative likelihood ratios (NLR) of each aforementioned rule were calculated and compared. Results: A total of 171 patients, with a mean age of 75.65±8.26 years and 48.54% male, were analysed in the study. Fifty-eight patients were reported to have experienced short-term adverse incidents during the month. The neurally mediated syncope group showed a significant sex-specific difference in adverse incidences but the cardiac syncope group did not. There were some factors associated with significant differences in adverse incidences, such as a history of hypertension, congestive heart failure, and chronic obstructive pulmonary disorder, as well as the levels of SpO2, B-type natriuretic peptide (BNP) and troponin T (TnT), while the levels of haemoglobin and creatinine suggested potential significance. In order of the ROSE, SFSR, FAINT, CSRS and BSC rules in the analysis, the sensitivities were 81.03%, 77.59%, 93.10%, 74.14% and 94.83%, the specificities were 86.73%, 84.96%, 38.94%, 60.18% and 56.64%, the NPVs were 89.91%, 88.07%, 91.67%, 81.93% and 95.52%, and the NLRs were 0.22, 0.26, 0.18, 0.43 and 0.09, respectively. Conclusions: This study revealed that the five mentioned rules for syncope risk stratification, with their own characteristics, all showed crucial significance for screening older adults. Therefore, physicians in the emergency department should flexibly understand and judge older patients’ potential risks according to the actual clinical situations.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".