A novel prediction model of mortality in chronic pancreatitis using clinical characteristics and gene promoter hypermethylation status
Bibliographic record
Abstract
Background Chronic pancreatitis (CP) is an inflammatory disease characterized by pain, functional deficits and increased mortality. The clinical course is unpredictable, and there are no classification systems or biomarkers to predict this. Identifying patients with high mortality risk is crucial for guiding clinical management and improving outcomes. This study presents a novel approach to a prognostic prediction model that combines clinical parameters and promoter hypermethylation (ph) of genes.Methods We performed methylation-specific quantitative polymerase chain reaction(qPCR) on a panel of 28 genes, using an accelerated bisulfite treatment protocol. We then developed a prognostic prediction model by backwards stepwise elimination using the methylation status of genes with a ph frequency > 5% and seven clinical factors. Survival was assessed with Kaplan-Meier survival curves and Cox regression.Results Ninety-seven patients with CP were included in the study. The final model included: Age, sex, exocrine insufficiency, diabetes, prior history of acute pancreatitis, and the methylation status of MLH1, HIC1, and RASSF1A. The model had an area under the curve (AUC) of 0.84 (95%CI: 0.76–0.92). A risk score was computed, and patients stratified into high and low-risk groups. The high-risk group had a significantly higher hazard ratio (HR) of death of 14.1 (95% CI; 4.3–46.0, p < 0.01).Conclusions This study serves as proof-of-concept that clinical factors can be combined with gene methylation status to provide additional prognostic information in patients with chronic pancreatitis. This could potentially aid the clinician in estimating which patients require intense follow-up. However, external validation is required.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".