Cancer cachexia and targeting chronic inflammation: a unified approach to cancer treatment and palliative/supportive care.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it