Simplified Early Predictors of Severe Acute Pancreatitis: A Prospective Study
Bibliographic record
Abstract
BACKGROUND: To propose simple tests for the prediction of severe acute pancreatitis (SAP), which are accurate and could be performed at emergency departments and outpatient clinics. METHODS: , creatinine, calcium, blood sugar, total protein, aspartate aminotransferase, total bilirubin, amylase, lipase and C-reaction protein were determined. Patients were graded into severe and mild acute pancreatitis based on CT Balthazar grading system. RESULTS: Twenty-seven patients were diagnosed to have SAP and 122 patients considered mild acute pancreatitis. Comparing parameters between both groups; significant factors (P < 0.05) were blood sugar level, haematocrit level, BMI and presence of pleural effusion in chest X-ray. The hematocrit at admission and at approximately 24 hours was significantly higher among patients with SAP. Twenty-two of 27 cases of severe disease and only 10 of 122 cases of mild acute pancreatitis diagnosed to have pleural effusion (P < 0.001). CONCLUSION: BMI, blood glucose ≥ 190 mg/dL, hematocrit level ≥ 43 % and pleural effusion detected by plain chest radiograph are simple tests and provide significant predictive power for clinical decision-making.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".