Major depression and electrovestibulography
Bibliographic record
Abstract
OBJECTIVES: No electrophysiological neuroimaging or genetic markers have been established that strongly relate to a diagnosis of major depression or its severity. The objective of this paper is to describe the preliminary evaluation of a potential new biomarker for depression utilizing the recording of electrical activity from the outer ear canal referred to as electrovestibulography (EVestG). METHODS: Sensory oto-acoustic features were extracted from EVestG data to compare 31 healthy age- and gender-matched individuals as controls to 43 major depressive disorder (MDD) subjects (22 symptomatic (MDD-S), 21 reduced symptomatic (MDD-R)). The stimulus was a single supine-vertical translation. The six features examined were based on the measured firing pattern interval histogram and the shape of the average field potential response. RESULTS: An unbiased classification accuracy of 85, 87 and 77% was achieved for separating Control from MDD-S, Control from MDD, and MDD-S from MDD-R groups respectively. Features used showed low but significant correlations (P < 0.05) with MADRS and CORE assessments. CONCLUSIONS: The results support the use of separate features for measuring MDD symptomatology versus diagnosing MDD, representing plausible different mechanisms of brain function in MDD-S and MDD-R. The first evidence of the successful application of sensory oto-acoustic features toward diagnosing and measuring the symptomatology of MDD is presented.
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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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".