Extended Experience with a Dynamic, Data-Driven Selective Drain Management Protocol in Pancreaticoduodenectomy: Progressive Risk Stratification for Better Practice
Bibliographic record
Abstract
BACKGROUND: Intraoperative drain use for pancreaticoduodenectomy has been practiced in an unconditional, binary manner (placement/no placement). Alternatively, dynamic drain management has been introduced, incorporating the Fistula Risk Score (FRS) and drain fluid amylase (DFA) analysis, to mitigate clinically relevant postoperative pancreatic fistula (CR-POPF). STUDY DESIGN: An extended experience with dynamic drain management was used at a single institution for 400 consecutive pancreaticoduodenectomies (2014 to 2019). This protocol consists of the following: drains omitted for negligible/low-risk FRS (0 to 2) and drains placed for moderate/high-risk FRS (3 to 10) with early (postoperative day [POD] 3) removal if POD1 DFA ≤5,000 U/L. Adherence to this protocol was prospectively annotated and outcomes were retrospectively analyzed. RESULTS: The overall CR-POPF rate was 8.7%, with none occurring in the negligible/low-risk cases. Moderate/high-risk patients manifested an 11.9% CR-POPF rate (n = 35 of 293), which was lower on-protocol (9.5% vs 21%; p = 0.014). After drain placement, POD1 DFA ≥5,000 U/L was a better predictor of CR-POPF than FRS (odds ratio 14.7; 95% CI, 4.3 to 50.3). For POD1 DFA ≤5,000 U/L, early drain removal was associated with fewer CR-POPFs (2.8% vs 23.5%; p < 0.001), and substantiated by multivariable analysis (odds ratio 0.09; 95% CI, 0.03 to 0.28). Surgeon adherence was inversely related to CR-POPF rate (R = 0.846). CONCLUSIONS: This extended experience validates a dynamic drain management protocol, providing a model for better drain management and individualized patient care after pancreaticoduodenectomy. This study confirms that drains can be safely omitted from negligible/low-risk patients, and moderate/high-risk patients benefit from early drain removal.
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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 | 0.016 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 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".