Sex Differences in All-Cause Inpatient Mortality Risk in Gastric Cancer: Nationwide Inpatient Population-Based Study
Bibliographic record
Abstract
Objective The purpose of this study is to evaluate the differences in demographic characteristics, comorbidities, and hospital outcomes in gastric cancer inpatients by sex and evaluate the risk factors for in-hospital mortality in gastric cancer inpatients by sex. Methods We conducted a cross-sectional study using the nationwide inpatient sample (NIS, 2019). Our sample included 22,415 adult inpatients (age ≥18 years) hospitalized with a primary discharge diagnosis of gastric cancer that was identified by the international classification of diseases, 10th revision (ICD-10) codes of C16.x. Independent univariate binomial logistic regression models were used to evaluate the odds ratio (OR) of predictors associated with all-cause in-hospital mortality in gastric cancer inpatients by sex. Results The total number of patients admitted with gastric cancer was 22,415, out of which 62.7% were males and 37.3% were females, with the mean age at the admission of 65.5 years and 66.4 years, respectively. While studying comorbidities, we found that 41.5% percent of all patients had gastric cancer with metastasis, and there existed a significantly higher prevalence in males (42.2% vs. 40.4% in females). Other important and statistically significant comorbid conditions that were prevalent in these patients include complicated diabetes (12.2%), obesity (12.1%), depression (8%), and alcohol abuse (3.1%). Females between 50-59 years of age were at 2.5 times increased risk of mortality compared to those less than 40 years of age (OR: 2.5; 95% CI: 1.28-4.95). Conclusion Females of the age group 50-59 years are at greater risk of all-cause inpatient mortality due to gastric cancer. Black males are at increased risk of all-cause inpatient mortality compared to White males. Gastric cancer incidence and mortality rates have been down trending with the development of screening and better treatment options, but it still continues to be a major burden on the healthcare system.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".