The Influence of Intraoperative Blood Loss on Fistula Development Following Pancreatoduodenectomy
Bibliographic record
Abstract
OBJECTIVE: To investigate the role of intraoperative estimated blood loss (EBL) on development of clinically relevant postoperative pancreatic fistula (CR-POPF) after pancreatoduodenectomy (PD). BACKGROUND: Minimizing EBL has been shown to decrease transfusions and provide better perioperative outcomes in PD. EBL is also felt to be influential on CR-POPF development. METHODS: This study consists of 5534 PDs from a 17-institution collaborative (2003-2018). EBL was progressively categorized (≤150mL; 151-400mL; 401-1,000 mL; > 1,000 mL). Impact of additive EBL was assessed using 20 3- factor fistula risk score (FRS) scenarios reflective of endogenous CR-POPF risk. RESULTS: CR-POPF developed in 13.6% of patients (N = 753) and median EBL was 400 mL (interquartile range 250-600 mL). CR-POPF and Grade C POPF were associated with elevated EBL (median 350 vs 400 mL, P = 0.002; 372 vs 500 mL, P < 0.001, respectively). Progressive EBL cohorts displayed incremental CR-POPF rates (8.5%, 13.4%, 15.2%, 16.9%; P < 0.001). EBL >400mL was associated with increased CR-POPF occurrence in 13/20 endogenous risk scenarios. Moreover, 8 of 10 scenarios predicated on a soft gland demonstrated increased CR-POPF incidence. Hypothetical projections demonstrate significant reductions in CR-POPF can be obtained with 1-, 2-, and 3-point decreases in FRS points attributed to EBL risk (12.2%, 17.4%, and 20.0%; P < 0.001). This is especially pronounced in high-risk (FRS7-10) patients, who demonstrate up to a 31% reduction (P < 0.001). Surgeons in the lowest-quartile of median EBL demonstrated CR-POPF rates less than half those in the upper-quartile (7.9% vs 18.8%; P < 0.001). CONCLUSION: EBL independently contributes significant biological risk to CR-POPF. Substantial reductions in CR-POPF occurrence are projected and obtainable by minimizing EBL. Decreased individual surgeon EBL is associated with improvements in 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 | 0.003 | 0.016 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".