Evaluation of postoperative outcomes of minimally invasive distal pancreatectomy for left-sided pancreatic tumors based on the modified frailty index: a retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: This study compared the postoperative outcomes of minimally invasive distal pancreatectomy (MIDP) for left-sided pancreatic tumors based on the modified frailty index (mFI). MATERIALS AND METHODS: This retrospective study included 2212 patients who underwent MIDP for left-sided pancreatic tumors between 2005 and 2019. Postoperative outcomes, including complications (morbidity and mortality), were analyzed using mFI, and the participants were divided into two groups: frail ( n =79) and nonfrail ( n =2133). A subanalysis of 495 MIDPs for pancreatic ductal adenocarcinoma was conducted to compare oncological outcomes. RESULTS: Clinically relevant postoperative pancreatic fistula was significantly higher in the frail group than in the nonfrail group. A significant between-group difference was observed in overall complications with Clavien-Dindo classification grade ≥III. Furthermore, the proportion of all complications before readmission was higher in the frail group than in the nonfrail group. Among all readmitted patients, the frail group had a higher number of grade ≥IV patients requiring ICU treatment. The frail group's 90-day mortality was 1.3%; the difference was statistically significant (nonfrail: 0.3%, P =0.021). In the univariate and multivariate logistic regression analyses, mFI ≥0.27 (odds ratio 3.231, 95% CI: 1.889-5.523, P <0.001), extended pancreatectomy, BMI ≥30 kg/m 2 , male sex, and malignancy were risk factors for Clavien-Dindo classification grade ≥III. CONCLUSION: mFI is a potential preoperative tool for predicting severe postoperative complications, including mortality, in patients who have undergone MIDP for left-sided tumors.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".