Pretreatment C-reactive protein/albumin ratio for predicting overall survival in pancreatic cancer
Bibliographic record
Abstract
BACKGROUND: Inconsistent findings have been reported regarding the association of C-reactive protein to albumin ratio (CAR) with survival outcome in patients with pancreatic cancer. We conducted the current meta-analysis to assess the prognostic utility of elevated baseline CAR in predicting overall survival (OS) in pancreatic cancer patients. METHODS: A comprehensively literature search was performed in the PubMed and Embase database until February 10, 2019. Studies evaluating the association between pretreatment CAR and OS among pancreatic cancer were selected. Study quality was evaluated by using the Newcastle-Ottawa Scale. RESULTS: Nine retrospective studies involving 1534 pancreatic cancer patients were identified. A meta-analysis using a random-effect model indicated that elevated CAR was associated with poor OS (hazard ratio 1.98; 95% confidence interval 1.58-2.48). Subgroup analysis produced similar prognostic values for OS in different geographical regions, sample sizes, thresholds of CAR, treating methods, and Newcastle-Ottawa Scale points. CONCLUSION: Elevated pretreatment CAR may independently predict poor OS in pancreatic cancer patients. Pretreatment CAR is possibly a simple and cost-effective blood-derived indicator for predicting survival outcome in patients with pancreatic cancer.
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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.010 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".