P.079 Factors influencing HINTS exam usage by Canadian Emergency Medicine Physicians
Bibliographic record
Abstract
Background: The HINTS examination is a sensitive and specific tool for determining whether a patient presenting with an acute vestibular syndrome has had a stroke. Despite its efficacy, it is often not used by Emergency Medicine (EM) physicians when assessing patients with vertigo. Methods: To ascertain why, we surveyed, by email, physicians registered with the Canadian Association of Emergency Physicians, to gather information on their practices when assessing patients with vertigo, and their utilization and perspectives concerning the HINTS examination. Results: 185 participants responded to our survey, demographically representative of Canadian EM physicians. The majority regularly use the HINTS exam in the appropriate setting, but significant minorities employ the exam inappropriately, such as in patients without nystagmus, with other neurological findings, or alongside tests for intermittent vertigo. Misapplication was associated with older age, years of practice, non-academic practice settings, and less residency training (p<0.05). The predominant reasons for not using this examination are lack of confidence in recalling and performing component exam techniques, particularly the head-impulse test, and doubts about the necessity, safety, or validity of this examination. Conclusions: HINTS examination use is limited by lack of provider skill, safety concerns, and doubts on its validity in excluding stroke when employed by EM physicians.
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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 | 0.002 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".