Diagnostic yield of second-line aetiological workup in patients with presumed idiopathic acute pancreatitis: a retrospective cohort study
Bibliographic record
Abstract
Background After an aetiological (first-line) workup, the cause of acute pancreatitis remains unidentified in a significant proportion of cases, a condition known as idiopathic acute pancreatitis (IAP).Methods Retrospective cohort study involving patients with presumed IAP referred for second-line aetiological workup. The completion of first-line aetiological evaluations was assessed upon referral, and the diagnostic outcomes of second-line investigations were evaluated. Over a one-year follow-up period, we documented acute pancreatitis recurrence and patient mortality. Recurrence risk was analysed using an age-adjusted Cox regression model, stratified by treatable versus non-treatable aetiologies.Results We identified 161 patients with presumed IAP, among whom 81 (50%) had recurrent acute pancreatitis. In total, 115 patients (71%) had a complete first-line aetiological workup. The overall diagnostic yield of the second-line aetiological workup was 25% (95% confidence interval [CI] 18–32%). Among second-line tests, the highest diagnostic yield was found for endoscopic ultrasound (34%, 95% CI 20–50%) and genetic testing (37%, 95% CI 22–53%). The most frequent aetiologies identified were biliary pancreatitis (16 patients [10%]) and pancreatitis with a genetic mutation (15 patients [9%]). Neoplasia was identified in two patients. A treatable aetiology was associated with a numerically reduced pancreatitis recurrence risk (Hazard Ratio 0.50, 95% CI 0.07–3.85, p = 0.51). No patient died during the follow-up period.Conclusion A second-line aetiological workup can identify the aetiology in 25% of patients with presumed IAP. The most frequent aetiologies are biliary pancreatitis and pancreatitis with a genetic mutation.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".