C-Reactive Protein Level: A Key Predictive Marker of Cachexia in Lymphoma and Myeloma Patients
Bibliographic record
Abstract
BACKGROUND: Cachexia is defined as an involuntary loss of weight, characterized by a loss of skeletal muscle mass with or without fat mass loss. It increases mortality risk and decreases quality of life in patients with lymphoma or myeloma. Early markers of cachexia are not identified. The objective of this work was to identify risk factor of cachexia in a cohort of patients with hematological malignancies to develop strategies to prevent cachexia and its consequences. METHODS: Clinical and biological parameters were collected before and at the end of the treatment. Quantification of weight loss during cachexia was performed by the method of Martin. Clinical responses to treatment of patients with lymphoma or myeloma were monitored. RESULTS: Thirty-eight percent of the 145 patients enrolled were cachectic at the end of treatment. Classical prognostic disease scores at the time of diagnosis seemed to be not associated with cachexia observed at the end of treatment. Only C-reactive protein (CRP) > 54 mg/L seemed to be a risk factor of cachexia (P = 0.023, odds ratio (OR): 5.94 (1.55 - 39.14), confidence interval (CI): 1.55 - 39.14). Those results were confirmed by bootstrap analysis. CONCLUSION: This study highlights that high CRP level at diagnosis seems to be a risk factor for cachexia during treatment, permitting to identify patients at risk and in future to implement preventive strategies.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".