Prediction of Postoperative Mortality in Patients With Organ Failure Following Pancreaticoduodenectomy
Bibliographic record
Abstract
Background Failure to rescue (FTR) patients with postoperative complications contribute to a significant proportion of postoperative mortality. Our main objective was to determine the risk factors for FTR among patients undergoing pancreaticoduodenectomy who suffered a life-threatening complication requiring intensive care unit (ICU) management. Materials and Methods Consecutive patients undergoing pancreaticoduodenectomy from 2011 to 2020 were reviewed retrospectively. Causes of organ failure were described as the one that most commonly contributed to patient’s transfer to ICU or death. Two groups were created based on whether patients had FTR and risk factors for FTR were compared. The impact of baseline characteristics, operative characteristics, and risk scoring on FTR was analyzed using multiple logistic regression. Results There were 19/58 (33%) FTR patients. Baseline, operative characteristics, postoperative complications, and length of hospital and ICU stay were similar between groups. However, a higher proportion of FTR patients experienced a postoperative pancreatic fistula (POPF) (16% vs 2.6%, P = .062). Among patients who experienced a POPF, the FTR group had a trend in delayed time from diagnosis to treatment (7 vs 23 hours, P=.131). Renal complications (OR 6.12, 95% CI, 1.23 to 38.43, P = .035) and time from POPF diagnosis to treatment (OR 1.05, 95% CI, 1.00 to 1.11, P = .036) were independent predictors of FTR by multivariable analysis. Conclusion The occurrence of certain postoperative complications such as renal complications as well as delayed timing of the management of POPF is predictive of FTR following pancreaticoduodenectomy, especially as delayed timing to treatment is a risk factor for FTR.
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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 | 0.004 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".