Clinical Implications of the C-Reactive Protein–Albumin Ratio as a Prognostic Marker in Terminally Ill Patients with Cancer
Bibliographic record
Abstract
Background:Few studies investigated the clinical implications of C-reactive protein–albumin ratio (CAR) in palliative care. Objectives:To determine the association of CAR with overall survival among terminally ill patients with cance. Measurements:Physicians recorded measures at the baseline. Patients were followed up to their death or observed for 6 months. The patients in cohort 2 were divided using the CAR cutoffs detected using a piecewise linear hazards model in cohort 1. We performed time-to-event analyses using the Kaplan–Meier method and log-rank tests and univariate and multivariate Cox regression analyses for patients in cohort 2. Results:A total of 1554 patients in cohort 1 and 1517 patients in cohort 2 were eligible. The cutoffs were 0.1, 1.2, and 6.4. The patients in cohort 2 were divided into four categories (<0.1 [n = 103], 0.1–1.2 [n = 433], 1.2–6.4 [712], and ≥6.4 [n = 269]). The adjusted p values of the log-rank tests were <0.001. Significantly higher risks of mortality were observed in the Cox proportional hazard model for the higher categories than in the lowest category (CAR 0.1–1.2: adjusted hazard ratio [HR] 1.49, 95% confidence interval [CI] 1.18–1.89; CAR 1.2–6.4: adjusted HR 2.08, 95% CI 1.65–2.62; CAR ≥6.4: adjusted HR 2.94, 95% CI 2.29–3.79). Conclusions:Patients with a higher CAR had significantly higher risks of mortality than those with a lower CAR.
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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 | 0.003 | 0.013 |
| 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 | 0.001 |
| 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".