The Role of Adipokines in Chronic Pancreatitis. A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND AND AIMS: Adipokines are among the biomarkers that have been studied in chronic pancreatitis (CP), as well as in pancreatic cancer (PC). So far, the existing findings are contradictory and inconclusive. Therefore, we assessed the levels of three major adipokines in CP in comparison to controls and PC, adiponectin, leptin, and resistin. METHODS: A systematic electronic search was carried out in November 2022 using PubMed, Embase, and Scopus, reviewing observational studies. By using the Newcastle-Ottawa Scale, the included studies' quality was evaluated (NOS). In the examination of the estimated overall effect size, we employed the random-effects model in conjunction with the mean difference (MD) analysis. The MD with 95% confidence interval (CI) served as the primary summary outcome. RESULTS: Our systematic review included a total of 14 studies, out of which nine were considered in our meta-analysis. A significant MD related to leptin levels in CP patients vs. controls (-1.299, 95%CI: -2.493 - -0.105), resistin levels in CP patients vs. controls (8.356, 95%CI: 3.700-13.012), and adiponectin levels in PC patients vs. controls (11.240, 95%CI: 5.872-16.60) was reported. However, no significant MD was reported in leptin levels between CP vs. PC patients (-0.936, 95%CI: -3.325-1.454), as well as adiponectin levels in CP patients vs. controls (0.422. 95%CI -5.651-6.535]) and in CP vs. PC patients (-6.252, 95%CI -13.269-0.766). CONCLUSIONS: CP was significantly associated with decreased leptin levels and increased resistin levels. Furthermore, increased levels of adiponectin are associated with PC. Yet, no significant MD was seen for leptin and adiponectin levels between CP and PC patients, and likewise for adiponectin levels between CP patients and controls. Results should be interpreted with caution due to the high heterogeneity between the included studies.
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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 | 0.017 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.040 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".