Peak urea level, leukocyte count and use of invasive ventilation as risk factors of mortality in acute pancreatitis: A retrospective study
Bibliographic record
Abstract
BACKGROUND: Acute pancreatitis (AP) is associated with high complications. Early, reliable prediction of mortality may improve patient management. METHODS: We retrospectively reviewed medical records of 1,599 patients with AP treated at a single large hospital in southwest China. Models to predict mortality were derived from a subset of 1,062 patients (development dataset), and the models were then validated in the remaining 537 patients (validation dataset). Independent risk factors and prediction models for mortality were identified using logistic regression. RESULTS: A total of 33 patients in the development dataset and 13 in the validation dataset died during hospitalization. Independent risk factors for mortality were found to be plasma urea levels, glucose levels and platelet counts at admission; as well as peak urea levels, leukocyte counts and use of invasive ventilation during hospitalization. Based on the development dataset, a mortality prediction model based only on urea level at admission gave an area under the curve (AUC) of 0.81, which did not significantly improve by incorporating glucose level or platelet count at admission. Significantly better was a model taking into account three in-hospital parameters: peak urea level, leukocyte count and use of invasive ventilation (AUC 0.97). CONCLUSIONS: While mortality of AP patients can be predicted reasonably well based only on urea values at admission, predictions are more reliable when they take into account in-hospital data on peak urea level, leukocyte count and use of invasive ventilation.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.001 |
| Research integrity | 0.000 | 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".