Usage of the HINTS exam and neuroimaging in the assessment of peripheral vertigo in the emergency department
Bibliographic record
Abstract
BACKGROUND: Dizziness is a common presenting symptom in the emergency department (ED). The HINTS exam, a battery of bedside clinical tests, has been shown to have greater sensitivity than neuroimaging in ruling out stroke in patients presenting with acute vertigo. The present study sought to assess practice patterns in the assessment of patients in the ED with peripherally-originating vertigo with respect to utilization of HINTS and neuroimaging. METHODS: A retrospective cohort study was performed using data pertaining to 500 randomly selected ED visits at a tertiary care centre with a final diagnostic code related to peripherally-originating vertigo between January 1, 2010 - December 31, 2014. RESULTS: A total of 380 patients met inclusion criteria. Of patients presenting to the ED with dizziness and vertigo and a final diagnosis of non-central vertigo, 139 (36.6%) received neuroimaging in the form of CT, CT angiography, or MRI. Of patients who did not undergo neuroimaging, 17 (7.1%) had a bedside HINTS exam performed. Almost half (44%) of documented HINTS interpretations consisted of the ambiguous usage of "HINTS negative" as opposed to the terminology suggested in the literature ("HINTS central" or "HINTS peripheral"). CONCLUSIONS: In this single-centre retrospective review, we have demonstrated that the HINTS exam is under-utilized in the ED as compared to neuroimaging in the assessment of patients with peripheral vertigo. This finding suggests that there is room for improvement in ED physicians' application and interpretation of the HINTS exam.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".