Diagnostic Accuracy of Neutrophil-Creatinine Index for Predicting Severe Acute Pancreatitis Using the Revised Atlanta Classification As Gold Standard
Bibliographic record
Abstract
INTRODUCTION: Early prediction of severity is important to guide treatment and triage. Many scoring systems and biomarkers are available, but none are both simple and highly accurate at admission. The aim of our study was therefore to determine the diagnostic accuracy of the neutrophil-creatinine index (NCI) in diagnosing severe acute biliary pancreatitis, using the revised Atlanta classification (2012) as the gold standard. METHODS: This cross-sectional validation study was conducted in the department of surgery, Benazir Bhutto Hospital, Rawalpindi, over a period of six months. A total of 217 patients with acute pancreatitis (AP) were included by non-probability consecutive sampling. The diagnosis was based on clinical, biochemical, and imaging criteria. The severity of AP was classified according to the revised Atlanta classification (2012), which served as the gold standard. The NCI was calculated at admission as absolute neutrophil count (× 10³/µL) × serum creatinine (mg/dL). A cut-off value of ≥11.27 was considered positive for severe AP (SAP). Diagnostic accuracy was assessed using sensitivity, specificity, predictive values, and receiver operating characteristic (ROC) curve analysis. RESULTS: Of 217 patients, 21 (9.7%) developed SAP. At the cut-off of 11.27, the NCI showed sensitivity 95.2%, specificity 91.3%, positive predictive value (PPV) 54.1%, negative predictive value (NPV) 99.4%, and overall accuracy 91.7%. The ROC analysis demonstrated excellent discrimination, with an area under the curve (AUC) of 0.96 (95% confidence interval (CI) 0.92-1.00). CONCLUSION: The NCI is a simple and inexpensive parameter that can predict SAP with high accuracy at admission. It may serve as a practical adjunct to existing scores and biomarkers, especially in resource-limited settings.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.000 |
| 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".