Predictors of All-Cause In-Hospital Mortality in Patients With Malignant Well-Differentiated Gastroenteropancreatic Neuroendocrine Tumors
Bibliographic record
Abstract
Background: Factors influencing in-hospital mortality in patients with well-differentiated gastroenteropancreatic neuroendocrine tumors (GEP-NETs) remain understudied, highlighting gaps in optimizing acute clinical outcomes. This study aimed to identify sociodemographic and clinical predictors of all-cause in-hospital mortality in this population. Methods: Using 2016 - 2020 data from the National Inpatient Sample (NIS), patients with malignant well-differentiated GEP-NETs were identified via the International Classification of Diseases, 10th Revision (ICD-10) codes. The primary outcome was in-hospital mortality. Sociodemographic and clinical variables (heart failure (HF), malnutrition, Charlson Comorbidity Index (CCI), and tumor site) were analyzed using multivariable logistic regression. Results: Among 5,642 patients (mean age 64, standard deviation (SD) 12.9), multivariable analysis identified HF (adjusted odds ratio (aOR) 2.09, 95% confidence interval (CI): 1.10 - 3.95), malnutrition (aOR 1.84, 95% CI: 1.29 - 2.62), pancreatic (aOR 1.52, 95% CI: 1.01 - 2.30) or colon tumors (aOR 2.31, 95% CI: 1.51 - 3.53), CCI ≥ 5 (aOR 1.49, 95% CI: 1.06 - 2.10), hypertension (aOR 0.65, 95% CI: 0.47 - 0.91) and elective admissions (aOR 0.40, 95% CI: 0.25 - 0.63) as clinically relevant factors associated with in-hospital mortality. Conclusions: Advanced age, tumor location, malnutrition, and HF may be critical mortality predictors among patients with GEP-NETs. These findings advocate for integrated care models prioritizing nutritional support, cardiovascular monitoring, and early elective interventions to improve outcomes.
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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 | 0.000 | 0.002 |
| 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 | 0.001 |
| 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".