Trends of Oncological Quality of Robotic Gastrectomy for Gastric Cancer in the United States
Bibliographic record
Abstract
Background: Robotic gastrectomy (RG) has been increasingly used for treatment of gastric cancer in the United States. However, it is unknown if there has been a nationwide improvement of short-term safety outcomes and oncological quality metrics over time. Methods: We used the National Cancer Database to identify patients who underwent major gastrectomy from 2010 through 2018. The short-term safety outcomes and oncological metrics were compared between cases of open gastrectomy (OG), laparoscopic gastrectomy (LG), and RG. We also compared the indications and outcomes of RG between the three periods (2010 - 2012, 2013 - 2015, and 2016 - 2018). Results: Of the 22,445 patients included, 1,867 (8%) underwent RG. Number of RG continued to increase from only 37 cases performed in 2010 to 412 cases performed in 2018. The number of lymph nodes (LNs) examined (OG, 16; LG, 17; and RG, 19) and the R0 rate (OG, 88%; LG, 92%; and RG 94%) were better for RG than for OG or LG (P < 0.001). In the RG group, the number of LNs examined (first period, 15; third period, 18; P < 0.001), R0 rate (first period, 88.6%; third period, 91.1%; P < 0.001), length of hospital stay (first period, 9 days; third period, 8 days; P < 0.001), 30-day readmission rate (first period, 10.1%; third period, 7.9%; P < 0.001), and 90-day mortality (first period, 7.3%; third period, 6.0%; P = 0.003) continued to improve cohort over time. The ratio of the robotic cases performed in academic institutions gradually increased (first period, 48.6%; third period, 54.3%; P < 0.001). In multivariable analyses, RG was associated with more than 15 LNs being examined (OR, 1.49; 95% CI, 1.34 - 1.65; P < 0.001). The indications for RG appeared expanding to include more advanced stage, high comorbidity, and patients who underwent preoperative therapy. Conclusions: RG has been increasingly performed in the past decade. Although its indication was expanded to include more advanced tumors, we found that the oncological quality metrics and safety outcomes of RG have improved over time and were better than those of OG or LG.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".