A meta-analysis examining clinical predictors of hippocampal volume in patients with major depressive disorder.
Bibliographic record
Abstract
BACKGROUND: Some, although not all, studies report small hippocampal volume in patients with major depressive disorder (MDD) relative to healthy controls. Here, we explore the contribution of key demographic and clinical variables to this difference. METHODS: We used meta-analytic techniques to provide an updated analysis of data from 32 magnetic resonance imaging studies of hippocampal volume in patients with MDD. RESULTS: Our analysis confirmed the difference in hippocampal volume, but only among patients with MDD whose duration of illness was longer than 2 years or who had more than 1 disease episode. We found no such effect in studies that included patients who did not fit these criteria. The effect was limited to children and middle-aged or older adults. Analyzed collectively, studies including young adult patients showed equivalent hippocampal volumes across MDD patients and controls, a result that may be attributable to a reduced burden of illness in this population. Age at onset of disease, severity of depression at the time of scanning, sex and slice thickness did not contribute to differences in hippocampal volume between patients with MDD and controls. LIMITATIONS: The small size of many of the clinical and demographic subgroups may have limited statistical power to detect between-group differences. CONCLUSION: Although all studies were cross-sectional, our results suggest that hippocampal volume reductions generally occur after disease onset in patients with MDD. These findings have implications for the timing of clinical interventions aimed at reducing the impact of MDD on neuronal structure and function.
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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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.028 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".