Effect of Double Layer Polyglycolic Acid Felt for Reducing Pancreatic Fistula After Pancreatoduodenectomy
Bibliographic record
Abstract
OBJECTIVE: To evaluate the usefulness of a double coating of polyglycolic acid (PGA) felt for pancreaticojejunostomy in reducing the incidence of clinically relevant postoperative pancreatic fistula (POPF) in patients with a normal pancreas. BACKGROUND: Despite pancreaticojejunostomy being an advanced procedure in patients undergoing pancreatoduodenectomy (PD), few studies have reported a satisfactory reduction in the incidence of POPF. METHODS: This study was an international multicenter randomized controlled trial conducted between October 2018 and December 2021. Patients with a main pancreatic duct <3 mm in diameter and soft pancreas undergoing pancreaticojejunostomy were eligible and randomized to either Arm A (conventional pancreaticojejunostomy) or Arm B (pancreaticojejunostomy using double coating of PGA felt). The primary endpoint was the incidence of grade B/C POPF. This trial was registered at ClinicalTrials.gov (NCT03331718) and Japan Registry of Clinical Trials (jRCTs042180090). RESULTS: A total of 514 patients were enrolled and randomly assigned to the study. The full analysis set population consisted of 508 patients, including 253 patients in Arm A and 255 patients in Arm B. According to the full analysis, the incidence of grade B/C POPF in Arm A was 28%, whereas that in Arm B was 25% (adjusted odds ratio: 0.97, 95% CI: 0.90-1.05; P =0.453). The incidence of intra-abdominal abscesses and mortality was also not significantly different between the 2 groups. CONCLUSIONS: This study showed that the use of a double coating of PGA felt in patients undergoing pancreaticojejunostomy did not reduce the incidence of grade B/C 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".