Association between arginine catabolism and major depressive disorder
Bibliographic record
Abstract
BACKGROUND: Alterations in the levels of arginine and its related catabolic products (ie, ornithine, citrulline, and argininosuccinate) in the urea and nitric oxide cycles were reported to play roles in the pathogenesis of major depressive disorder (MDD). The aim of this meta-analysis study is to explore the associations between arginine with its related catabolic products and MDD, and to discuss the possible role of arginine catabolism in the pathoetiology of MDD. METHODS: This study will be conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The English language literature published in the databases of PubMed, EMBASE, PsycINFO and Web of Science will be systematically searched. Forest plots will be used to estimate the associations between arginine and its related catabolic products with MDD. Subgroup analysis and meta-regression will also be performed to investigate the source of the potential heterogeneity. Sensitivity analysis will be performed to strengthen the results and to investigate whether any single study would have a significant effect on the results of meta-analysis. Publication bias will be tested for using the funnel plot with Begg test and Egger test. The Newcastle-Ottawa Scale will be applied to assess the risk of bias of observational studies. RESULTS: An integrated assessment of arginine with its related catabolic products may contribute to predict the risk of MDD. ETHICS AND DISSEMINATION: The results of associations between arginine with its related catabolic products and MDD will be reported in a peer-reviewed publication. With our findings from this meta-analysis, we hope to provide the most up-to-date evidence for the contributions of arginine and related catabolic products to predict the risk of MDD. SYSTEMATIC REVIEW REGISTRATION: The protocol of current meta-analysis has been registered at the Open Science Framework [Available at: https://doi.org/10.17605/osf.io/7fn59].
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".