Lacunar stroke, deep white matter disease and depression: a meta-analysis
Bibliographic record
Abstract
BACKGROUND: Lacunar stroke is a small (<2 cm) infarction that accounts for approximately 20% of all strokes. While a third of all stroke patients experience depressive symptoms, the prevalence of depression in the lacunar stroke patient population is unclear. This meta-analysis aimed to synthesize the evidence on the effect of lacunar stroke and deep white matter disease on depressive symptoms. METHODS: A systematic search of electronic databases was conducted, resulting in the inclusion of 12 studies. Analyses were performed on the effects of lacunar stroke, volume and location of lacunes on depression prevalence, and the effect on depression severity. The effects estimates were calculated in random-effects models. RESULTS: None of the analyses produced statistically significant results. Lacunar stroke patients had a non-significantly higher prevalence of depression compared to patients with non-lacunar cerebrovascular diseases (OR = 1.46, 95% CI: 0.88-2.43, p = 0.15). Neither thalamic (OR = 1.37 (0.85-2.20), p = 0.19), deep white matter (RR = 1.16 (0.85-1.57), p = 0.35), multiple lacunes (OR = 1.34 (0.81-2.22), p = 0.25), or the volume of lacunes (MD = -4.71 (-351.59-342.18), p = 0.98) had an effect on depression prevalence. Lastly, lacunar stroke did not influence depressive symptom severity (MD = 0.96 (-1.57-3.48), p = 0.46). CONCLUSIONS: The pooled group of patients with lacunar stroke and deep white matter disease appear to have a similar prevalence of depression compared to those with other types of cerebrovascular diseases. However, the small number of studies, heterogeneous comparison groups, and high statistical heterogeneity between studies posed an obstacle to the meta-analysis. To determine appropriate screening and treatment approaches, future research will need to separate lacunar stroke and deep white matter disease patients, and include larger sample sizes and healthy control groups to determine their distinct contributions to depression.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.041 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".