Bibliographic record
Abstract
It has become clear that the inflammatory immune system is altered during the course of clinical depression. In particular, studies on human patients have found depression to be associated with disturbances in the trafficking of cells of the adaptive immune system, coupled with elevations of innate immune messengers and pro-inflammatory cytokines. Paralleling these findings, stressor-based animal models of depression have implicated several cytokines, most notably interleukin-1β (IL-1β), IL-6, and tumor necrosis factor-α. Elevations of these cytokines and general inflammatory indicators, such as C-reactive protein, together with reductions of specific immune cells (e.g., T lymphocytes) might serve as useful biomarkers of depression or at least, certain subtypes of the disorder. Recent reports also suggest the possibility that anti-inflammatory agents could have therapeutic value in acting as adjunct treatments with traditional anti-depressants. Along these lines, we presently discuss the evidence for pro-inflammatory cytokine involvement in depression, as well as the possibility that anti-inflammatory agents and trophic cytokines themselves might have important anti-depressant properties.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".