Clinical and Economic Comparison of Laparoscopic to Open Liver Resections Using a 2-to-1 Matched Pair Analysis: An Institutional Experience
Bibliographic record
Abstract
BACKGROUND: Surgical resection of hepatic lesions is associated with intraoperative and postoperative morbidity and mortality. Our center has introduced a laparoscopic liver resection (LLR) program over the past 3 years. Our objective is to describe the initial clinical experience with LLR, including a detailed cost analysis. STUDY DESIGN: We evaluated all LLRs from 2006 to 2010. Each was matched to 2 open cases for number of segments removed, patient age, and background liver histology. Model for End-Stage Liver Disease (MELD) and the Charlson comorbidity index were calculated retrospectively. Nonparametric statistical analysis was used to compare surgical and economic outcomes. Analyses were performed including and excluding converted cases. RESULTS: Fifty-seven patients underwent attempted LLR. Demographic characteristics were similar between groups. Estimated blood loss was lower in the LLR vs the open liver resection (OLR) group, at 250 mL and 500 mL, respectively (p < 0.001). Median operating room times were 240 minutes and 270 minutes in the LLR and OLR groups, respectively (p = 0.14). Eight cases were converted to open (14%): 2 for bleeding, 2 for anatomic uncertainty, 1 for tumor size, 1 for margins, 1 for inability to localize the tumor, and 1 for adhesions. Median length of stay was lower for LLR at 5 days vs 6 days for OLR (p < 0.001). There was no difference in frequency of ICU admission, reoperation, 30-day emergency room visit, or 30-day readmission rates. Median overall cost for LLR was lower at $11,376 vs $12,523 for OLR (p = 0.077). CONCLUSIONS: Our experience suggests that LLR confers the clinical advantages of reduced operating room time, estimated blood loss, and length of stay while decreasing overall cost. LLR, therefore, appears to be a clinically and fiscally advantageous approach in properly selected patients.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".