The Atlanta Classification, Revised Atlanta Classification, and Determinant-Based Classification of Acute Pancreatitis
Bibliographic record
Abstract
OBJECTIVES: To determine which classification is more accurate in stratifying severity. METHODS: The study used a retrospective analysis of a prospective acute pancreatitis database (June 2005-December 2007). Acute pancreatitis severity was stratified according to the Atlanta classification (AC) 1992, the revised Atlanta classification (RAC) 2012, and the determinant-based classification (DBC) 2012. Receiver operating characteristic analysis (area under the curve) compared the accuracy of each classification. Logistic regression identified predictors of mortality. RESULTS: 338 patients were analyzed: 13% had persistent organ failure (POF) (>48 hours), of whom 37% had multisystem POF, and 11% had pancreatic necrosis, of whom 19% had infected necrosis. Mortality was 4.1%. For predicting mortality (area under the curve), the RAC (0.91) and DBC (0.92) were comparable (P = 0.404); both outperformed the AC (0.81) (P < 0.001). For intensive care unit admission, the RAC (0.85) and DBC (0.85) were comparable (P = 0.949); both outperformed the AC (0.79) (P < 0.05). There were 2 patients in the critical category of the DBC. Multisystem POF was an independent predictor of mortality (odds ratio, 75.0; 95% confidence interval, 13.7-410.6; P < 0.001), whereas single-system POF, sterile necrosis, and infected necrosis were not. CONCLUSION: The RAC and DBC were generally comparable in stratifying severity. The paucity of patients in the critical category in the DBC limits its utility. Neither classification accounts for the impact of multisystem POF, which was the strongest predictor of mortality.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".