Anxiety Disorder: Measuring the Impact on Major Depressive Disorder
Bibliographic record
Abstract
Background: About half of all Major Depressive Disorder (MDD) patients have anxiety disorder. There is a neurologic basis for the comorbidity of balance (vestibular) disorders and anxiety. To detect comorbid anxiety disorder in MDD patients and, importantly, to investigate its relationship with depressive severity, we use Electrovestibulography (EVestG), which is predominantly a measure of vestibular response. Methods: In a population of 42 (26 with anxiety disorder) MDD patients, EVestG signals were measured. Fourteen (eight with anxiety disorder) were not on any anti-depressants, anti-psychotics or mood stabilizers. Using standard questionnaires, participants were depression-wise labelled as reduced symptomatic (MADRS ≤ 19, R) or symptomatic (MADRS > 19, S) as well as with or without anxiety disorder. Analyses were conducted on the whole data set, matched (age/gender/MADRS) subsets and compared with medication free subsets. Low-frequency EVestG firing pattern modulation was measured. Results: The main differences between MDD populations with and without anxiety disorder populations, regardless of being medicated or not, were (1) the presence of an increased 10.8 Hz component in the dynamic movement phase recordings, (2) the presence of asymmetric right versus left 7.6–8.9 Hz and 12.1–13.8 Hz frequency bands in the no motion (static) phase recordings, and (3) these differences were dependent on depressive severity. Conclusions: The EVestG measures are capable of quantifying anxiety in MDD patients. These measures are functions of depressive severity and are hypothesized to be linked to Hippocampal Theta (~4–12 Hz).
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".