Procalcitonin as a Predictor of Mortality in Patients With Severe Acute Pancreatitis
Bibliographic record
Abstract
Background: Acute pancreatitis (AP) is a severe inflammatory disorder that begins with the inappropriate activation of pancreatic enzymes within acinar cells due to biliary reflux, alcohol abuse, gallstones, and autoimmune disease. Several biomarkers have been studied that may aid in the early detection of pancreatic necrosis. The aim of this project was to evaluate the usefulness of procalcitonin (PCT) in predicting mortality in patients with severe AP in Mexican population. Methods: An observational study, including 59 patients diagnosed with AP from 2018 to 2023, was conducted in a tertiary care hospital. Serum PCT levels were assessed on the first and third days of hospitalization (24 and 72 h). Results: A total of 59 patients were included, and the main etiologies were lithiasis (28 patients, 47.5%) and endoscopic retrograde cholangiopancreatography (ERCP) (nine patients, 15.3%). Of the total patients, 16 (27.1%) died during their hospital stay, and the main etiologies were septic shock of abdominal origin (10 patients, 62.5%) followed by extra-abdominal shock (six patients, 37.5%). The average PCT level was 4.54 ± 8.12 on the first day of hospital stay, and 5.20 ± 10.90 at 72 h. The cut-off point was 1.26 ng/mL with the best sensitivity and specificity of PCT as a predictor of mortality at 72 h of 75% and 68%, respectively (area under the curve 0.7, 95% confidence interval (CI): 0.61 - 0.88), and positive and negative predictive values of 0.46 and 0.87, respectively. Conclusions: We propose the usefulness of PCT as a biochemical marker to predict mortality in patients with severe AP due to its accessibility in the hospital environment. We propose to carry out studies with more patients and follow-up times. In addition, it is necessary to consider other biomarkers associated with PCT to help us improve the positive predictive value of mortality in this disease.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".