Major depression and electrovestibulography
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 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 | 0.001 |
| 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 it