Missed Strokes Using Computed Tomography Imaging in Patients With Vertigo
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The purpose of this study was to determine the proportion of emergency department (ED) patients with a diagnosis of peripheral vertigo who received computed tomography (CT) head imaging in the ED and to examine whether strokes were missed using CT imaging. METHODS: This population-based retrospective cohort study assessed patients who were discharged from an ED in Ontario, Canada, with a diagnosis of peripheral vertigo, April 2006 to March 2011. Patients who received CT imaging (exposed) were matched by propensity score methods to patients who did not (unexposed). If performed, CT imaging was presumed to be negative for stroke because brain stem/cerebellar stroke would result in hospitalization. We compared the incidence of stroke within 30, 90, and 365 days subsequent to ED discharge between groups, to determine whether the exposed group had a higher frequency of early strokes than the matched unexposed group. RESULTS: Among 41 794 qualifying patients, 8596 (20.6%) received ED head CT imaging, and 99.8% of these patients were able to be matched to a control. Among exposed patients, 25 (0.29%) were hospitalized for stroke within 30 days when compared with 11 (0.13%) among matched nonexposed patients. The relative risk of a 30- and 90-day stroke among exposed versus unexposed patients was 2.27 (95% confidence interval, 1.12-4.62) and 1.94 (95% confidence interval, 1.10-3.43), respectively. There was no difference between groups at 1 year. Strokes occurred at a median of 32.0 days (interquartile range, 4.0-33.0 days) in exposed patients, compared with 105 days (interquartile range, 11.5-204.5) in unexposed patients. CONCLUSIONS: One fifth of patients diagnosed with peripheral vertigo in Ontario received imaging that is not recommended in guidelines, and that imaging was associated with missed strokes.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".