The Pattern of Change in Depressive Symptoms and Inflammatory Markers After Electroconvulsive Therapy
Bibliographic record
Abstract
ABSTRACT: Depression is a major mental health disorder, and its pathophysiology is still largely unknown, as is the action mechanism of electroconvulsive therapy (ECT). Some evidence suggests that inflammation might play a role in depression, and several studies have attempted to demonstrate a link between ECT and cytokines. This systematic review used a qualitative analysis to assess the effect of ECT on inflammatory markers as it relates to the clinical response of depressive symptoms in major depressive disorders. The bibliographic search engines CINAHL, Embase, PsychInfo, and PubMed were used to identify articles published up to July 2020. Search terms related to depression, ECT, and inflammation were used. Descriptive statistical analyses were performed to relate changes in inflammatory markers to clinical response to ECT. Twenty-five studies were included in the analysis. No systematic increases or decreases were found in a given inflammatory marker over the ECT; however, we observed that tumor necrosis factor α and interleukin-6 (IL-6) were more often found to be decreased after ECT, whereas IL-8 and IL-10 were more often found to be increased after treatment. No trend in correlation was found between the degree of clinical improvement of depressive symptoms and the variation of any inflammatory markers, despite positive clinical response to ECT. Great heterogeneity with regard to methodology used and lack of power of the studies included in this review could explain the lack of systematic change and correlation found in this study. Future research conducted on this subject should take into account these methodological limitations to allow subsequent meta-analysis.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".