A Systematic Review of Clinical Vestibular Symptom Triage, Tools, and Algorithms
Bibliographic record
Abstract
OBJECTIVE: The evaluation of peripheral vestibular disorders in clinical practice is an especially difficult endeavor, particularly for the inexperienced clinician. The goal of this systematic review is thus to evaluate the design, approaches, and outcomes for clinical vestibular symptom triage and decision support tools reported in contemporary published literature. DATA SOURCES: as well as an exhaustive set of terms to encompass vestibular disorders. REVIEW METHODS: Study characteristics, tool metrics, and performance were extracted using a standardized form. Quality assessment was conducted using a modified version of the Quality of Diagnostic Accuracy Studies 2 (QUADAS-2) assessment tool. RESULTS: A total of 18 articles each reporting a novel tool for the evaluation of vestibular disorders were identified. Tools were organized into 3 discrete categories, including self-administered questionnaires, health care professional administered tools, and decision support systems. Most tools could differentiate between specific vestibular pathologies, with outcome measures including sensitivity, specificity, and accuracy. CONCLUSION: A multitude of tools have been published to aid with the evaluation of vertiginous patients. Our systematic review identified several low-evidence reports of triage and decision support tools for the evaluation of vestibular disorders.
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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 | 0.017 | 0.094 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".