Bibliographic record
Abstract
Depression and fatigue are conditions responsible for heavy global societal burden, especially in patients already suffering from chronic diseases. These symptoms have been identified by those affected as some of the most disabling symptoms which affect the quality of life and productivity of the individual. While many factors play a role in the development of depression and fatigue, both have been associated with increased inflammatory activation of the immune system affecting both the periphery and the central nervous system (CNS). This is further supported by the well-described association between diseases that involve immune activation and these symptoms in autoimmune disorders, such as multiple sclerosis and immune system activation in response to infections, like sepsis. Treatments for depression also support this immunopsychiatric link. Antidepressants have been shown to decrease inflammation, while higher levels of baseline inflammation predict lower treatment efficacy for most treatments. Those patients with higher initial immune activation may on the other hand be more responsive to treatments targeting immune pathways, which have been found to be effective in treating depression and fatigue in some cases. These results show strong support for the hypothesis that depression and fatigue are associated with an increased activation of the immune system which may serve as a valid target for treatment. Further studies should focus on the pathways involved in these symptoms and the development of treatments that target those pathways will help us to better understand these conditions and devise more targeted treatments.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".