Evaluation of a multimodal diagnostic algorithm for prediction of cognitive impairment in elderly patients with dizziness
Bibliographic record
Abstract
BACKGROUND: The current diagnostic workup for chronic dizziness in elderly patients often neglects neuropsychological assessment, thus missing a relevant proportion of patients, who perceive dizziness as a subjective chief complaint of a concomitant cognitive impairment. This study aimed to establish risk prediction models for cognitive impairment in chronic dizzy patients based on data sources routinely collected in a dizziness center. METHODS: 7.1 years, 41.0% women) with chronic dizziness were prospectively characterized by (1) neuro-otological testing, (2) quantitative gait assessment, (3) graduation of focal brain atrophy and white matter lesion load, and (4) cognitive screening (MoCA). A linear regression model was trained to predict patients' total MoCA score based on 16 clinical features derived from demographics, vestibular testing, gait analysis, and imaging scales. Additionally, we trained a binary logistic regression model on the same data sources to identify those patients with a cognitive impairment (i.e., MoCA < 25). RESULTS: = 0.49; mean absolute error: 1.7). The most important risk-predictors of cognitive impairment were age (β = - 0.75), pathological Romberg's sign (β = - 1.05), normal caloric test results (β = - 0.8), slower timed-up-and-go test (β = - 0.67), frontal (β = - 0.6) and temporal (β = - 0.54) brain atrophy. The binary classification yielded an area under the curve of 0.84 (95% CI 0.70-0.98) in distinguishing between cognitively normal and impaired patients. CONCLUSIONS: The need for cognitive testing in patients with chronic dizziness can be efficiently approximated by available data sources from routine diagnostic workup in a dizziness center.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".