The Risk of Infection-Caused Mortality in Gastric Adenocarcinoma: A Population-Based Study
Bibliographic record
Abstract
Background: Gastric adenocarcinoma (GAC) is a deadly tumor. Postoperative complications, including infections, worsen its prognosis and may affect overall survival. Little is known about perioperative complications as well as modifiable and non-modifiable risk factors. Early detection and treatment of these risk factors may affect overall survival and mortality. Methods: We extracted GAC patient's data from the Surveillance, Epidemiology, and End Results (SEER) database and analyzed using Pearson's Chi-square, Cox regression, Kaplan-Meier, and binary regression methods in SPSS. Results: At the time of analysis, 59,580 GAC patients were identified, of which 854 died of infection. Overall, mean survival in months was better for younger patients, age < 50 years vs. ≥ 50 years (60.45 vs. 56.75), and in females vs. males (65.23 vs. 53.24). The multivariate analysis showed that the risk of infectious mortality was higher in patients with age ≥ 50 years (hazard ratio (HR): 3.137; 95% confidence interval (CI): 2.178 - 4.517), not treated with chemotherapy (HR: 1.669; 95% CI: 1.356 - 2.056), or surgery (HR: 1.412; 95% CI:1.132 - 1.761) and unstaged patients (HR: 1.699; 95% CI: 1.278 - 2.258). In contrast, the mortality risk was lower in females (HR: 0.658; 95% CI: 0.561 - 0.773) and married patients (HR: 0.627; 95% CI: 0.506 - 0.778). The probability of infection was higher in older patients (odds ratio (OR) of 2.094 in ≥ 50 years), other races in comparison to Whites and Blacks (OR: 1.226), lesser curvature, not other specified (NOS) as a primary site (OR: 1.325), and patients not receiving chemotherapy (OR: 1.258). Conclusion: Older, unmarried males with GAC who are not treated with chemotherapy or surgery are at a higher risk for infection-caused mortality and should be given special attention while receiving treatment.
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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".