Feeding Tube Placement in Nutritionally At-risk Patients Undergoing Pancreaticoduodenectomy: A Propensity Score-matched Analysis
Bibliographic record
Abstract
BACKGROUND: Routine feeding tube (FT) placement during pancreaticoduodenectomy (PD) has not reduced complication rates. Nonetheless, FTs are often selectively placed in patients with poor nutritional status such as those with significant weight loss or hypoalbuminemia, based on the assumption that the benefits outweigh the risks. This study examined whether FT placement reduced complications in at-risk nutritional subgroups. METHODS: This retrospective cohort study used data from the National Surgical Quality Improvement Program (NSQIP) database (2017-2020). Patients undergoing PD were grouped according to nutritional status as follows: (1) weight Loss >10%, (2) albumin <30 g/L, (3) weight loss or low albumin, (4) both weight loss and low albumin, and (5) neither. Five propensity score matching (PSM) models were utilized, each adjusted for perioperative variables, and postoperative outcomes between patients with and without FTs across these groups were compared. Each subgroup analysis served as an independent evaluation of the potential benefits of FT placement in the defined populations. RESULTS: Among 16,780 patients with PD, 742 (4.4%) received FTs. PSM analyses showed that FT placement did not reduce complications in any subgroup, but significantly increased delayed gastric emptying rates, especially in patients with low albumin and weight loss (29.4% vs 16.0%, P < 0.0001). FT placement also led to longer hospital stays (11 vs 8 d, P < 0.0001) and higher reoperation rates in 2 groups. CONCLUSIONS: In nutritionally at-risk patients undergoing pancreaticoduodenectomy, the risks of feeding tube placement appear to outweigh the benefits, as no reduction in complications was observed, and morbidity was increased.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".