Inflammatory Cytokines in Children and Adolescents with Depressive Disorders: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Objective: Meta-analytic evidence shows alterations of peripheral inflammatory cytokines in adults with depressive disorders. By contrast, no evidence synthesis on alterations of peripheral inflammatory cytokines in children/adolescents with depressive disorders is available to date. To fill this gap, we conducted a systematic review and meta-analysis of case–control studies comparing serum cytokine levels in children/adolescents with depressive disorders and healthy controls. Methods: Based on a preregistered protocol (PROSPERO-CRD42018095418), we searched PubMed, Ovid, and Web of Knowledge from inception through July 21, 2018, with no language restrictions, and contacted study authors for unpublished data/information. Random-effects model was used to compute effect size for each cytokine. The Newcastle–Ottawa Scale was used to asses study bias. Results: From a pool of 4231 nonduplicate, potentially relevant references, 8 studies were retained for the qualitative synthesis and 5 for the meta-analysis. TNF-α was higher in participants with depressive disorders versus controls, falling short of statistical significance. Conclusions: Overall, due to the small number of studies, in contrast to the literature in adults, further evidence is needed to confirm possible inflammatory alterations associated with depression in youth.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.026 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".