Preoperative C-Reactive Protein-to-Albumin Ratio Predicts Postoperative Pancreatic Fistula following Pancreatoduodenectomy: A Single-Center, Retrospective Study
Bibliographic record
Abstract
Postoperative pancreatic fistula (POPF) following pancreatoduodenectomy (PD) is a potentially lethal complication, and it is clinically important to determine its risk preoperatively. Although C-reactive protein-to-albumin ratio (CAR) is reported to be a prognostic marker for postoperative complications in several cancers, no evidence is currently available regarding the association between preoperative CAR and POPF following PD for periampullary tumors. This study examined whether preoperative CAR could predict POPF following PD. Clinical data were retrospectively retrieved from Ehime University Hospital. The optimal cut-off value for CAR was determined using receiver operating characteristic (ROC) curve analysis. This study enrolled 203 consecutive patients undergoing PD for periampullary tumors. The CAR value was significantly higher in the POPF group than in the non-POPF group (p < 0.001). According to the ROC curve analysis, the optimal cut-off value for CAR was 0.09. Patients with CAR ≥ 0.09 had higher incidence rates of POPF than their counterparts. CAR ≥ 0.09 was a risk factor for POPF in the multivariate logistic regression analysis (odds ratio 34.5, 95% confidence interval 11.75–101.38, p < 0.001). This is the first report demonstrating an association between CAR and POPF following PD. Preoperative CAR is an independent predictive marker for POPF following PD.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".