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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".