Cancer cachexia and targeting chronic inflammation: a unified approach to cancer treatment and palliative/supportive care.
Bibliographic record
Abstract
Chronic inflammation often acts as a tumor promoter, resulting in aggressive cancerous growth and spread. Many of the same inflammatory factors that promote tumor growth also are responsible for cancer cachexia/anorexia, pain, debilitation, and shortened survival. A compelling case may be made for mounting an attack on inflammation with other anticancer measures at initial diagnosis, with the consequent probability of improving both patient quality of life and survival. High serum levels of the inflammatory marker C-reactive protein or fibrinogen and an elevated white blood cell count correlate with poor prognosis and may be used as a prognostic index to establish the need for nutritional/metabolic intervention. At the author's institution, a concerted effort is being made to screen all newly diagnosed patients with non-small cell lung cancer for the presence of nutritional problems, inflammatory markers, and related symptoms. Interventions include dietary counseling; nutritional and, if warranted, vitamin supplementation; exercise concordant with the patient's physical condition; a prescription for omega 3 fatty acids if inflammation is present, and general symptom management. To establish the value of early nutritional/metabolic intervention, clinical trials are needed that combine measures that combat cachexia and inflammation with first-line chemotherapy in patients who present with weight loss, fatigue, and deteriorating function.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".