Validation of the <scp>Sudbury Vertigo Risk Score</scp> to risk stratify for a serious cause of vertigo
Bibliographic record
Abstract
INTRODUCTION: In 2022, nearly 0.5 million Canadians visited an emergency department (ED) for dizziness, accounting for over 3.5% of all ED visits. Of these patients, only 2%-5% received a serious diagnosis. The cost of ED and inpatient care for dizziness in Canada exceeds $200 million per year, of which neuroimaging accounts for a large proportion. Over one-third of dizziness patients undergo a CT scan of the head, 96% of which are negative. Despite extensive investigation, patients discharged with a benign dizziness diagnosis have a 50-fold increased risk of being admitted to the hospital within 7 days with a diagnosis of stroke. Our study aimed to derive a clinical risk score to guide the investigation and referral for serious causes of vertigo in ED patients. METHODS: This multicenter historical cohort study was conducted over 7 years at three university-affiliated tertiary care EDs. Patients presenting with vertigo, dizziness, or imbalance were recruited. The main outcome was an adjudicated serious diagnosis, defined as stroke, transient ischemic attack, vertebral artery dissection, or brain tumor. We estimated a sample size of 4450 patients, based on a 2% prevalence of serious outcomes, to evaluate the sensitivity with 95% confidence intervals (CIs). RESULTS: A total of 4559 patients were enrolled (mean age 78.1 years, 57.8% women), with serious events occurring in 104 (2.3%) patients. The C-statistic was 0.95 (95% CI 0.92-0.98). The risk of a serious diagnosis ranged from 0% for a score of <5 to 16.7% for a score >8. Sensitivity for a serious diagnosis was 100% (95% CI 96.5%-100%) and specificity was 69.2% (95% CI 67.8%-70.51%) for a score <5. CONCLUSION: The Sudbury Vertigo Risk Score effectively identifies the risk of a serious diagnosis in patients with dizziness. Thus, it guides further investigation, consultation, and treatment decisions and ultimately improves resource utilization and reduces missed diagnoses.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".