Association of preoperative <scp>CT‐scan</scp> features and clinically relevant postoperative pancreatic fistula after pancreaticoduodenectomy: a <scp>meta‐analysis</scp>
Bibliographic record
Abstract
BACKGROUND: Clinically relevant postoperative pancreatic fistula (CR-POPF) is a significant complication after pancreaticoduodenectomy. CR-POPF is associated with various adverse outcomes, including high mortality rates. Identifying complication predictors for CR-POPF, such as preoperative CT scan features, including pancreatic attenuation index (PAI) and pancreatic duct diameter (PDD), is critical. This systematic review and meta-analysis consolidate existing literature to assess the impact of these variables on CR-POPF risk. METHODS: Our comprehensive search, conducted in May 2023, covered PubMed, Scopus, Embase, and Web of Science databases. Inclusion criteria encompassed peer-reviewed cohort studies on pancreaticoduodenectomy, focusing on preoperative CT scan data. Case reports, case series, and studies reporting distal pancreatectomy were excluded. The quality assessment of included articles was done using New-Castle Ottawa Scale for cohort studies. Statistical analysis was carried out using Review Manager 5. This study was registered at the International Prospective Register of Systematic Reviews database (PROSPERO) on 12 May 2023 (registration number: CRD42023414139). RESULTS: We conducted a detailed analysis of 38 studies with 7393 participants. The overall incidence of CR-POPF was 24%. Multiple linear regression analyses revealed that PDD and pancreatic parenchymal thickness were significantly associated with CR-POPF. CONCLUSION: Our systematic review and meta-analysis shed light on CT scan findings for predicting CR-POPF after Whipple surgery. Age, PDD, and pancreatic parenchymal thickness significantly correlate with CR-POPF.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.029 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".