Assessing the Predictive Value of D-dimer in Acute Pancreatitis: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Introduction: Acute Pancreatitis (AP) often leads to multi-organ dysfunction with high morbidity and mortality necessitating early identification for optimal management. Traditional severity scores have limitations, prompting exploration of biomarkers like D-dimer. Aim: To evaluate D-dimer’s accuracy as a severity marker in AP when compared to theother biomarkers. Materials and Methods: The present comprehensive search was conducted on multiple databases. The authors included randomised clinical trials, cohort, cross-sectional, and casecontrol studies with adults diagnosed with AP and D-dimer measurements. Non-human studies, case reports, and nonEnglish articles were excluded. Risk of bias was assessed using the Newcastle-Ottawa Scale. Data were analysed with R software focusing on diagnostic accuracy. Results: Nineteen studies met the inclusion criteria. Most were retrospective with predominantly male participants. The pooled sensitivity for D-dimer in identifying Severe AP (SAP) was 0.85 (95% CI: 0.78-0.91), and specificity was 0.58 (95% CI: 0.31-0.85). The AUC for diagnostic accuracy was 0.75 (95% CI: 0.66-0.83). For severity assessment, sensitivity was 0.77 (95% CI: 0.71-0.83), specificity was 0.75 (95% CI: 0.67- 0.83), and AUC was 0.78 (95% CI: 0.73-0.83). D-dimer had 0.86 sensitivity for organ failure detection (AUC 0.72, 95% CI: 0.63-0.81). Conclusion: D-dimer shows moderate-to-high accuracy in identifying SAP and predicting organ failure. It is a promising, cost-effective, and easily accessible biomarker for early severity assessment. Further research is needed to confirm its clinical role and integration into severity models.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".