Standardization of early drain removal following pancreatic resection: proposal of the “Ottawa pancreatic drain algorithm”
Bibliographic record
Abstract
Abstract Background Early drain removal after pancreatic resection is encouraged for individuals with low postoperative day 1 drain amylase levels (POD1 DA) to mitigate associated morbidity. Although various protocols for drain management have been published, there is a need to assess the implementation of a standardized protocol. Methods The Ottawa pancreatic drain algorithm (OPDA), based on POD1 DA and effluent volume, was developed and implemented at our institution. A retrospective cohort analysis was conducted of all patients undergoing pancreatic resection January 1, 2016-October 30, 2017, excluding November and December 2016 (one month before and after OPDA implementation). Results 42 patients pre-implementation and 53 patients post-implementation were included in the analysis. The median day of drain removal was significantly reduced after implementation of the OPDA (8 vs. 5 days; p = 0.01). Early drain removal appeared safe with no difference in reoperation or readmission rate after protocol implementation (p = 0.39; p = 0.76). On subgroup analysis, median length of stay was significantly shorter following OPDA implementation for patients who underwent DP and did not develop a postoperative pancreatic fistula (POPF) (6 vs 10 days, p = 0.03). Although the incidence of both surgical site infection and POPF were reduced following the intervention, neither reached statistical significance (38.1 to 28.3%, p = 0.31; and 38.1 to 28.3%, p = 0.31 respectively). Conclusions Implementing the OPDA was associated with earlier drain removal and decreased length of stay in patients undergoing distal pancreatectomy who did not develop POPF, without increased morbidity. Standardizing drain removal may help facilitate early drain removal after pancreatic resection at other institutions.
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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 | 0.026 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".