Weight Loss Following Hepatopancreatobiliary Surgery. How Much is Too Much? A Retrospective Cohort Study
Bibliographic record
Abstract
Background & Aims. Postoperative weight loss is common following hepato-pancreato-biliary (HPB) surgical resections; however, the extent of weight loss and the association with poor outcomes have not been well described. We assessed the average percentage of weight loss and risk factors associated with sustained postoperative weight loss. Materials and Methods. We enrolled patients undergoing major HPB surgical resections from 2011–2016 at a single institution. We evaluated percent change in weight postoperatively, incidence of complications, and nutritional clinical markers at 1, 3, and 6 months postoperatively compared to preoperative baseline. We used multiple logistic regression to evaluate factors associated with significant weight loss (>10% from baseline) at 3 months from surgery. Results. Among 262 patients undergoing HPB surgery, liver surgery patients lost 2.5% of baseline weight at 3 months postoperatively but regained baseline weight by 6 months. Pancreatic surgery patients lost 7.7% at 3 months and were unable to recover their baseline weights at 6 months. Forty-three (16%) patients had major postoperative complications including abdominal abscess (5.3%) and anastomotic leak (3.8%). Patients who experienced major postoperative complications had a greater percentage weight loss at 3 months compared to those without major complications: median 11% (interquartile range (IQR): 7%–15%) vs 4% (IQR: 0%–8%), P < .001. In the multivariable analysis, major postoperative complications were associated with significant weight loss at 3 months (OR 3.39, 95% CI 1.38–8.33). Conclusions. Due to the association of weight loss and major postoperative complications, patients who experience significant weight loss (>10% from baseline) may benefit from nutritional assessment for dietary intervention.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".