Development of a clinical risk score to risk stratify for a serious cause of vertigo: A prospective cohort study
Bibliographic record
Abstract
Abstract Objectives Identify high-risk clinical characteristics for a serious cause of vertigo in patients presenting to the emergency department. Design Multicentre prospective cohort study over 3 years. Setting Three university-affiliated tertiary care emergency departments. Participants Patients presenting with vertigo, dizziness or imbalance. A total of 2078 of 2618 potentially eligible patients (79.4%) were enrolled (mean age 77.1 years; 59% women). Main outcome measurements An adjudicated serious diagnosis defined as stroke, transient ischemic attack, vertebral artery dissection or brain tumour. Results 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] 0.92–0.98). The risk of a serious diagnosis ranged from 0% for a score of <5, 2.1% for a score of 5-8, and 41% for a score >8. Sensitivity for a serious diagnosis was 100% (95% CI, 97.1-100%) and specificity 72.1% (95% CI, 70.1-74%) for a score <5. Conclusions The Sudbury Vertigo Risk Score identifies the risk of a serious diagnosis as a cause of a patient’s vertigo and can 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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".