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Record W3012097956 · doi:10.1111/acem.13960

Can Emergency Physicians Accurately Rule Out a Central Cause of Vertigo Using the HINTS Examination? A Systematic Review and Meta‐analysis

2020· review· en· W3012097956 on OpenAlexaff
Robert Ohle, Renee‐Anne Montpellier, Virginie Marchadier, Aidan Wharton, Sarah McIsaac, Mackenzie Anderson, David W. Savage

Bibliographic record

VenueAcademic Emergency Medicine · 2020
Typereview
Languageen
FieldNeuroscience
TopicVestibular and auditory disorders
Canadian institutionsLaurentian UniversityNOSM UniversityScience North
Fundersnot available
KeywordsMedicineVertigoEmergency departmentNystagmusBenign paroxysmal positional vertigoMEDLINEMeta-analysisNauseaStroke (engine)Physical examinationPediatricsPhysical therapyRadiologySurgeryInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

Abstract Introduction Dizziness is a common complaint presented in the emergency department (ED). A subset of these patients will present with acute vestibular syndrome (AVS). AVS is a clinical syndrome defined by the presence of vertigo, nystagmus, head motion intolerance, ataxia, and nausea/vomiting. These symptoms are most often due to benign vestibular neuritis; however, they can be a sign of a dangerous central cause, i.e., vertebrobasilar stroke. The Head Impulse test, Nystagmus, Test of Skew (HINTS) examination has been proposed as a bedside test for frontline clinicians to rule out stroke in those presenting with AVS. Our objective was to assess the diagnostic accuracy of the HINTS examination to rule out a central cause of vertigo in an adult population presenting to the ED with AVS. Our aim was to assess the diagnostic accuracy when performed by emergency physicians versus neurologists. Methods We searched PubMed, Medline, Embase, the Cochrane database, and relevant conference abstracts from 2009 to September 2019 and performed hand searches. No restrictions for language or study type were imposed. Prospective studies with patients presenting with AVS using criterion standard of computed tomography and/or magnetic resonance imaging were selected for review. Two independent reviewers extracted data from relevant studies. Studies were combined if low clinical and statistical heterogeneity was present. Study quality was assessed using the QUADAS‐2 tool. Random effects meta‐analysis was performed using RevMan 5 and SAS 9.3. Results A total of five studies with 617 participants met the inclusion criteria. The mean (±SD) study length was 5.3 (±3.3) years. Prevalence of vertebrobasilar stroke ranged 9.3% to 44% (mean ± SD = 39.1% ± 17.1%). The most common diagnoses were vertebrobasilar stroke (mean ± SD = 34.8% ± 17.1%), peripheral cause (mean ± SD = 30.9% ± 16%), and intracerebral hemorrhage (mean ± SD = 2.2% ± 0.5%). The HINTS examination, when performed by neurologists, had a sensitivity of 96.7% (95% CI = 93.1% to 98.5%, I 2 = 0%) and specificity of 94.8% (95% CI = 91% to 97.1%, I 2 = 0%). When performed by a cohort of physicians including both emergency physicians (board certified) and neurologists (fellowship trained in neurootology or vascular neurology) the sensitivity was 83% (95% CI = 63% to 95%) and specificity was 44% (95% CI = 36% to 51%). Conclusions The HINTS examination, when used in isolation by emergency physicians, has not been shown to be sufficiently accurate to rule out a stroke in those presenting with AVS.

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 imitation

Not 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.

metaresearch head score (Codex)0.015
metaresearch head score (Gemma)0.055
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.023
Threshold uncertainty score0.081

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.055
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0230.030
Bibliometrics0.0070.007
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0030.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.227
GPT teacher head0.423
Teacher spread0.196 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

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".

Quick stats

Citations110
Published2020
Admission routes1
Has abstractyes

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