The Effects of Standard Pharmacotherapy on Oxidative Stress Markers in Bipolar Patients: A Systematic Review
Bibliographic record
Abstract
Background: Bipolar disorder is a complex mental disorder with a multifactorial pathology. Meta-analyses have shown that bipolar disorder is associated with imbalanced oxidative status, which can contribute to disease progression. Therefore, restoring the oxidative imbalance could be beneficial in the treatment of bipolar disorder. The firstline pharmacotherapy for bipolar disorder is lithium, anticonvulsants (particularly sodium valproate), and atypical antipsychotics. In the present review, we sought to describe the effects of these first-line medications on oxidative stress in bipolar patients. Methods: We systematically searched databases through January 2022, including the Web of Science, PubMed, Scopus, and Embase, with no language or time restrictions. Eligible articles that assessed oxidative markers in bipolar patients following standard pharmacotherapy were included. Result: According to Newcastle Ottawa and NIH scales, the overall quality of the included articles was low, and their heterogeneity prevented us from performing a meta-analysis. Conclusion: We found that standard medications, especially lithium, can potentially alleviate oxidative imbalance based on a reduction in oxidative markers, such as TBARs and MDA, although randomized clinical trials are needed to unequivocally confirm these results.
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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".