Passive Versus Active Intra‐Abdominal Drainage Following Pancreatic Resection: Does A Superior Drainage System Exist? A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
Postoperative pancreatic fistula (POPF) is a major source of morbidity following pancreatic resection. Surgically placed drains under suction or gravity are routinely used to help mitigate the complications associated with POPF. Controversy exists as to whether one of these drain management strategies is superior. The objective was to identify and compare the incidence of POPF, adverse events, and resource utilization associated with passive gravity (PG) versus active suction (AS) drainage following pancreatic resection. MEDLINE, EMBASE, CINAHL, and Cochrane Library databases were searched from inception to May 18, 2020. Outcomes of interest included POPF, post-pancreatectomy hemorrhage (PPH), surgical site infection (SSI), other major morbidity, and resource utilization. Descriptive qualitative and pooled quantitative meta-analyses were performed. One randomized control trial and five cohort studies involving 10 663 patients were included. Meta-analysis found no difference in the odds of developing POPF between AS and PG (p = 0.78). There were no differences in other endpoints including PPH (p = 0.58), SSI (wound p = 0.21, organ space p = 0.05), major morbidity (p = 0.71), or resource utilization (p = 0.72). The risk of POPF or other adverse outcomes is not impacted by drain management following pancreatic resection. Based on current evidence, a suggestion cannot be made to support the use of one drain over another at this time. There is a trend toward increased intra-abdominal wound infections with AS drains (p = 0.05) that merits further investigation.
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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 | 0.014 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.032 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".