Development of a Clinical Risk Score to Risk Stratify for a Serious Cause of Vertigo in Patients Presenting to the Emergency Department
Bibliographic record
Abstract
STUDY OBJECTIVE: Identify high-risk clinical characteristics for a serious cause of vertigo in patients presenting to the emergency department (ED). METHODS: Multicentre prospective cohort study over 3 years at three university-affiliated tertiary care EDs. Participants were patients presenting with vertigo, dizziness or imbalance. Main outcome measurement was an adjudicated serious diagnosis defined as stroke, transient ischemic attack, vertebral artery dissection or brain tumour. RESULTS: A total of 2,078 of 2,618 potentially eligible patients (79.4%) were enrolled (mean age 77.1 years; 59% women). Serious events occurred in 111 (5.3%) patients. We used logistic regression to create a 7-item prediction model: male, age over 65, hypertension, diabetes, motor/sensory deficits, cerebellar signs/symptoms and benign paroxysmal positional vertigo diagnosis (C-statistic 0.96, 95% confidence interval [CI] 0.92 to 0.98). The risk of a serious diagnosis ranged from 0% for a score of <5, 2.1% for a score of 5 to 8, and 41% for a score >8. Sensitivity for a serious diagnosis was 100% (95% CI, 97.1% to 100%) and specificity 72.1% (95% CI, 70.1% to 74%) for a score <5. CONCLUSION: The Sudbury Vertigo Risk Score identifies the risk of a serious diagnosis as a cause of a patient's vertigo and if validated could assist physicians in guiding further investigation, consultation, and treatment decisions, improving resource utilization and reducing 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 | 0.004 | 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 | 0.001 |
| 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".