Integrated diagnostic algorithm for acute vertigo combining TiTrATE, STANDING, and HINTS: a validation study in the emergency department.
Bibliographic record
Abstract
Accurate diagnosis of acute vertigo (AV) in emergency settings is crucial due to varied underlying causes. Challenges include differentiating non-life-threatening conditions, like vestibular migraine, from severe issues, such as stroke. The "TiTrATE - STANDING Adapted" algorithm was created to help non-specialist emergency physicians diagnose posterior circulation strokes in AV patients, overcoming the limitations of current practices that require specialized knowledge and equipment. This study involved a prospective validation and retrospective analysis of 67 patients at the National Hospital for Neurology and Neurosurgery and University College London Hospital. Patients underwent objective oculomotor assessments through video oculography and pure tone audiometry, conducted by an experienced audiologist in the acute stage. The accuracy of the "TiTrATE - STANDING Adapted" algorithm was compared to final diagnoses made by specialists, which included a comprehensive review of medical histories, objective test results, and imaging studies. The "TiTrATE - STANDING Adapted" algorithm demonstrated a sensitivity of 90%, with low specificity (57.9%), resulting in a high rate of false positives (24 out of 67) and a global accuracy of 62.7%. Conditions such as vestibular migraine and chronic vascular issues (e.g., orthostatic hypotension) were often misclassified, impacting the overall specificity. Integrating TiTrATE, HINTS Plus, and STANDING into a single diagnostic algorithm for acute vertigo in the ED could enhance accuracy and streamline decision-making. However, the combined model must perform at least as well as its individual components. Key improvements needed before implementation include adding vestibular migraine criteria, refining stroke exclusion guidelines, and ongoing validation to boost diagnostic precision and patient outcomes.
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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 | 0.008 | 0.023 |
| 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 | 0.001 |
| 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".