Bibliographic record
Abstract
PURPOSE OF REVIEW: As the 'monoamine hypothesis of depression' fails to explain all aspects of major depression, additional causes are being investigated. Several observations suggest that inflammatory mechanisms pay a role in the cause of major depressive disorder (MDD). This article reviews their role in major depression. RECENT FINDINGS: Recent studies support the concept that inflammatory mechanisms play a crucial role in the pathomechanisms of major depression. Major depression shares similarities with 'sickness behavior', a normal response to inflammatory cytokines. Elevations in proinflammatory cytokines and other inflammation-related proteins in major depression were found in plasma and cerebrospinal fluid (CSF) as well as in postmortem studies. Elevated levels of proinflammatory cytokines persist after clinical symptoms of depression are in remission and can also predict the onset of a depressive episode. Antidepressant treatment can lead to a normalization of elevated cytokine levels in major depression. Finally, we understand how inflammatory mechanisms affect the metabolism of tryptophan and how nonsteroidal antiinflammatory drugs (NSAIDs) can interfere with the effects of antidepressants. SUMMARY: Further studies are needed to fully understand the role of inflammatory mechanisms in major depression and the potential treatment implications.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".