Loudness dependent auditory evoked potentials and suicidality in depression – A meta-analysis with replication in unmedicated patients
Bibliographic record
Abstract
OBJECTIVE: Major Depressive Disorder (MDD) is a risk factor for suicide. Loudness-Dependent Auditory Evoked Potentials (LDAEP) is an electroencephalographic biomarker of central serotonergic activity associated with MDD and suicidality. Yet, studies on the matter are conflicting. We therefore: 1) Conducted a meta-analysis to clarify the relation between LDAEP, past suicide attempts and current suicidality in MDD, and 2) replicated the findings in independent data of unmedicated patients with MDD. METHODOLOGY: 1) 10 studies were included in the meta-analysis. The difference in scalp LDAEP between patients with and without past suicide attempts was estimated using Hedge's g. The correlation between scalp LDAEP and suicidality was estimated using Fisher's Zr. The Newcastle-Ottawa Scale was used to assess methodological differences. The review was preregistered (PROSPERO). 2) We examined the associations above within an independent cohort of 88 unmedicated patients with MDD. The analyses were preregistered (AsPredicted.org) and used to update the meta-analysis. RESULTS: In the initial meta-analysis, scalp LDAEP was not associated with past suicide attempts (g = 0.11 [-0.23, 0.46], p = 0.53) or correlated with suicidality (Zr = 0.06 [-0.15, 0.27], p = 0.60). In the independent cohort, scalp LDAEP was also not associated with past suicide attempts (g = 0.05 [-0.56, 0.65] t(86) = 0.15, p = 0.88) or correlated with suicidality (Zr = 0.11 [-0.1, 0.33] r = 0.11 [-0.10, 0.32], p = 0.30). In the updated meta-analysis, scalp LDAEP continued not to be associated with suicide attempts (g = 0.10 [-0.19, 0.39], p = 0.51) or suicidality (Zr = 0.06 [-0.12, 0.24], p = 0.50). CONCLUSION: LDAEP is not associated with past suicide attempts or suicidality in MDD, and LDAEP's usefulness in suicide prevention seems limited.
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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 | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.033 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".