Evaluating Laparoscopic and Robotic Liver Resection in Elderly Patients: A NSQIP Analysis of Short‐Term Outcomes
Bibliographic record
Abstract
INTRODUCTION: Results of minimally invasive laparoscopic (LLR) and robotic liver resection (RLR) have been promising, but the benefits in the elderly patients are still unclear. This study aims to compare short-term outcomes of LLR and RLR in elderly patients. METHODS: The 2017-2021 NSQIP database was analyzed comparing patients ≥ 65 years old undergoing LLR versus RLR. Postoperative outcomes, factors associated with complications and mortality were assessed using propensity score matched (PSM) and multivariable logistic regression. RESULTS: We analyzed 2,210 patients undergoing LLR (n = 1865,84.4%) and RLR (n = 345,15.6%). Patients undergoing LLR were older (72.4 vs. 71.8 years; p = 0.04) and more likely to have ASA 4 (11.1% vs. 4.9%; p = 0.001). RLR patients had shorter hospital stays (3.5 vs. 4.4 days; p < 0.001) but longer operative durations (221.4 vs. 203.5 min; p = 0.013). On adjusted analyses, RLR was not associated with increased odds of serious complications (OR: 0.82, CI95% 0.42-1.58, p = 0.545) or mortality (OR: 0.87, p = 0.851). After PSM, RLR statistically reduced length of stay (-0.72 days; p = 0.012) but increased operative times ( + 32.62 min; p < 0.001). Subgroup analysis of patients ≥ 75 years confirmed consistent findings. CONCLUSIONS: RLR provides comparable safety and short-term outcomes to LLR, offering shorter hospital stays but longer operative durations. Findings support RLR as a viable option in elderly patients, but further studies evaluating long-term outcomes are warranted.
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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 | 0.004 |
| 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 | 0.000 | 0.000 |
| 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".