Economic evaluation of applying the Canadian Syncope Risk Score in an Australian emergency department
Bibliographic record
Abstract
OBJECTIVE: To evaluate the Canadian Syncope Risk Score (CSRS) in syncope patients presenting to the ED from an economic perspective, using very-low and low-risk patients (CSRS -3 to 0) as a threshold for avoiding hospital admissions. METHODS: A decision-analytic model, specifically a decision-tree, was developed to evaluate application of the CSRS. A hypothetical cohort of 1000 patients was modelled based on characteristics and outcome of patients enrolled in a clinical validation study performed alongside this evaluation. Several analytic based approaches were used to handle model outputs and uncertainties. RESULTS: For a cohort of 1000 patients, applying the CSRS was associated with 169 less inpatient admissions from the ED, when compared to usual care. There was also a cost-saving of $8255 per admitted patient, when the CSRS was applied, compared to usual care. Adopting the CSRS was the optimal approach in all scenario analyses and was robust to changes in model parameters. More than three-quarters (78.6%) of all model simulations showed that applying the CSRS is a cost-saving approach to managing syncope. There was high confidence in all results, with the approach using the CSRS reducing the costs and number of syncope-related hospital admissions. CONCLUSIONS: Compared to usual care, applying the CSRS appeared as a cost-effective strategy. This new evidence will help decision-makers choose cost-effective approaches for the management of patients presenting to the ED with syncope, as they search for efficient ways to maximise health gain from a finite budget.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.031 | 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".