Are the symptoms of cancer and cancer treatment due to a shared biologic mechanism?
Bibliographic record
Abstract
BACKGROUND: Cancers and cancer treatments produce multiple symptoms that collectively cause a symptom burden for patients. These symptoms include pain, wasting, fatigue, cognitive impairment, anxiety, and depression, many of which co-occur. There is growing recognition that at least some of these symptoms may share common biologic mechanisms. METHODS: In November 2001, basic and clinical scientists met to consider evidence for a cytokine-immunologic model of symptom expression along with directions for future research. RESULTS: The characteristics of cytokine-induced sickness behavior in animal models have much in common with those of symptomatic cancer patients. Sickness behavior refers to a set of physiologic and behavioral responses observed in animals after the administration of infectious or inflammatory agents or certain proinflammatory cytokines. In some cases, these responses can be prevented by cytokine antagonists. A combination of animal and human research suggests that several cancer-related symptoms may involve the actions of proinflammatory cytokines. CONCLUSIONS: Based on the similarities between cancer symptoms and sickness behavior, the authors discussed approaches to further test the implications of the relationship between inflammatory cytokines and symptoms for both symptom treatment and symptom prevention.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".