Establishment and Diagnostic Value of an Early Prediction Model for Acute Pancreatitis Complicated With Acute Kidney Injury
Bibliographic record
Abstract
OBJECTIVES: To establish an early prediction model for acute pancreatitis (AP) complicated with acute kidney injury (AKI) and evaluate its diagnostic value. METHOD: AP patients were recruited from the Emergency Department at Peking University People's Hospital in 2021 and stratified into AKI and control (no AKI) groups. Their clinical data were analyzed. The risk for AKI development was determined using logistic analyses to establish a risk prediction model, whose diagnostic value was analyzed using a receiver operating characteristic curve. RESULTS: There was no significant difference in the basic renal function between the AKI (n = 79) and control (n = 179) groups. The increased triglyceride glucose index (odds ratio [OR], 2.613; 95% confidence interval [CI], 1.324-5.158; P = 0.006), age (OR, 1.076; 95% CI, 1.016-1.140; P = 0.013), and procalcitonin (OR, 1.377; 95% CI, 1.096-1.730, P = 0.006) were associated with AKI development. A model was established for prediction of AKI (sensitivity 79.75%, specificity 96.65%). The area under the receiver operating characteristic curve was 0.856 which was superior to the Ranson, Bedside Index for Severity in AP, and Acute Physiology and Chronic Health Evaluation II scores (0.856 vs 0.691 vs 0.745 vs 0.705). CONCLUSIONS: The prediction model based on age, triglyceride glucose, and procalcitonin is valuable for the prediction of AP-related AKI.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| 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".