Laparoscopic versus open hepatic resection in patients ≥75 years old: A NSQIP analysis evaluating 2674 patients
Bibliographic record
Abstract
BACKGROUND: Previous studies report promising outcomes with minimally invasive (MIS) hepatectomy in elderly patients but remain limited by small size. This study aims to comparatively evaluate the demographics and outcomes of geriatric patients undergoing MIS and open hepatectomy. METHOD: The 2016-2021 NSQIP database was evaluated comparing patients ≥75 undergoing MIS versus open hepatectomy. Patient selection and outcomes were compared using bivariate analysis with multivariable modeling (MVR) evaluating factors associated with serious complications and mortality. Propensity score matched (PSM) analysis further evaluated serious complications, mortality, length of stay (LOS), Clavien Dindo Classification (CDC), and Comprehensive Complication Index (CCI) for cohorts. RESULTS: We evaluated 2674 patients with 681 (25.5%) receiving MIS hepatectomy. MIS approaches were used more for partial lobectomy (85.9% vs. 61.7%; p < 0.001), and required fewer biliary reconstructions (1.6% vs. 10.6%; p < 0.001). Patients were similar with regards to sex, body mass index, and other comorbidities. Unadjusted analysis demonstrated that MIS approaches had fewer serious complications (8.8% vs. 18.7%; p < 0.001). However, after controlling for cohort differences the MIS approach was not associated with reduced likelihood of serious complications (odds ratio [OR]: 0.77; p = 0.219) or mortality (OR: 1.19; p = 0.623). PSM analysis further supported no difference in serious complications (p = 0.403) or mortality (p = 0.446). However, following PSM a significant reduction in LOS (-1.99 days; p < 0.001), CDC (-0.26 points; p = 0.016) and CCI (-2.79 points; p = 0.022) was demonstrated with MIS approaches. CONCLUSIONS: This is the largest study comparing MIS and open hepatectomy in elderly patients. Results temper previously reported outcomes but support reduced LOS and complications with MIS approaches.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.000 |
| 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".