Systemic Inflammation and Nutritional Status in Patients on Palliative Cancer Care: A Systematic Review of Observational Studies
Bibliographic record
Abstract
Objective: This systematic literature review explores the results of studies that have analyzed the association between inflammation and nutritional status in patients with cancer in palliative care. Methods: The bibliographic research was performed in May 2019, according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Group guidelines. The inclusion criteria were papers that (1) had an online abstract available, (2) were original, (3) used a cohort or cross-sectional design, (4) involved patients with advanced cancer in palliative care, and (5) assessed the association between inflammation and nutritional status. The quality assessment was performed using the Newcastle-Ottawa Scale. Results: Nine studies were selected. Weight loss (WL; n = 7) was the most common nutritional marker employed and C-reactive protein (CRP; n = 6) was the most common inflammatory marker. There was considerable variability (39.0%-92.2%) in the proportion of patients who had WL in a 6-month period, while CRP >5 mg/dL was common in 45.3% to 73.9% of patients. Systemic inflammation was related to nutritional status, highlighting the relationship between CRP and WL and lean mass (LM). Patients with CRP >10 mg/L have been found to have a lower LM ( P < .001) and a faster rate of loss of LM at a faster rate during the disease trajectory ( P = .030). Conclusion: Nutritional status is associated with systemic inflammatory response. Inflammatory markers should be considered an additional parameter for the nutritional diagnosis of patients with cancer in palliative care.
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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 | 0.015 | 0.062 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.012 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".