A Cytokine-Based Neuroimmunologic Mechanism of Cancer-Related Symptoms
Bibliographic record
Abstract
While many of the multiple symptoms that cancer patients have are due to the disease, it is increasingly recognized that pain, fatigue, sleep disturbance, cognitive dysfunction and affective symptoms are treatment related, and may lead to treatment delays or premature treatment termination. This symptom burden, a subjective counterpart of tumor burden, causes significant distress. Progress in understanding the mechanisms that underlie these symptoms may lead to new therapies for symptom control. Recently, some of these symptoms have been related to the actions of certain cytokines that produce a constellation of symptoms and behavioral signs when given exogenously to both humans and animals. The cytokine-induced sickness behavior that occurs in animals after the administration of infectious or inflammatory agents or certain proinflammatory cytokines has much in common with the symptoms experienced by cancer patients. Accordingly, we propose that cancer-related symptom clusters share common cytokine-based neuroimmunologic mechanisms. In this review, we provide evidence from clinical and animal studies that correlate the altered cytokine profile with cancer-related symptoms. We also propose that the expression of coexisting symptoms is linked to the deregulated activity of nuclear factor-kappa B, the transcription factor responsible for the production of cytokines and mediators of the inflammatory responses due to cancer and/or cancer treatment. These concepts open exciting new avenues for translational research in the pathophysiology and treatment of cancer-related symptoms.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".