Competence of final year otolaryngology residents with the bedside head impulse test
Bibliographic record
Abstract
BACKGROUND: The bedside head impulse test (bHIT) is a clinical method of assessing the vestibulo-ocular reflex (VOR). It is a critical component of the bedside assessment of dizzy patients, and can help differentiate acute stroke from vestibular neuritis. However, there is evidence showing the bHIT is often not performed in appropriate clinical settings or is performed poorly. To date, there have been no studies evaluating the bHIT competence of graduating physicians. METHODS: 23 final year Otolaryngology -Head &Neck Surgery (OTL-HNS) residents in Canada were evaluated on the use of bHIT using a written multiple-choice examination, interpretation of bHIT videos, and performance of a bHIT. Ratings of subject bHIT performance were completed by two expert examiners (DT, DL) using the previously published Ottawa Clinic Assessment Tool (OCAT). RESULTS: Using a cut-off of an OCAT score of 4 or greater, only 22% (rater DT) and 39% (rater DL) of residents were found able to perform the bHIT independently. Inter-rater reliability was fair (0.51, interclass correlation). The mean scores were 65% (14.1% standard deviation) on the video interpretation and 71% (20.2% standard deviation) on the multiple-choice questions. The scores on multiple choice examination did not correlate with bHIT ratings (Pearson r = 0.07) but there was fair correlation between video interpretation and bHIT ratings (Pearson r = 0.45). CONCLUSION: Final year OTL-HNS residents in Canada are not adequately trained in performing the bHIT, though low interrater reliability may limit the evaluation of this bedside skill. Multiple choice examinations do not reflect bHIT skill. These findings have implications for development of competency-based curricula and evaluations in Canada in critical physical exam skills.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".