Postoperative Mortality Rates following Total Gastrectomy for Gastric Cancer: Experience of a Single Surgical Team
Bibliographic record
Abstract
Objective: Total gastrectomy is a complex surgical procedure that is characterized by significant postoperative morbidity and mortality rates and the patients may continue to experience adverse events beyond the standard 30-day follow-up period after surgery. The aim of this study is to investigate postoperative complication and 30-day/90-day mortality rates following total gastrectomy in a cohort of patients and highlight the possible ways that can improve the short-term outcome of this surgical procedure. Material and Methods: 401 patients underwent total gastrectomy for gastric carcinoma by a single surgical team from January 2001 till December 2021. The patients stratified in 3 groups based on the time period when surgery was performed, hospital- and treatment-related objective factors: group I (61 patients); group II (163 patients) and group III (177 patients). Results: Esophagojejunal anastomotic leakage rate varied with periods and ranged from 1.1% to 3.3%, 1.7% in total. Differences were insignificant in complication and death rates between the patients aged ≤70 years and > 70 years (p>0.05). Postoperative death rate within 90 days was higher by 42% compared to that in standard 30-day follow-up period. Neaodjuvant chemotherapy was not found to be associated with an increased rate of 30-day postoperative mortality (p>0.05). Conclusion(s): Total gastrectomy for gastric cancer may be associated with minimal rates of esophagojejunal anastomotic leakage and mortality when performed by specialized and experienced high-volume surgeons. 90-day mortality rate after total gastrectomy is 42% higher compared to 30-day mortality that warrants more intense monitoring of the relevant patients within 3 months after surgery.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".