Demographic Undertones for Sepsis Mortality in a Community-Based Hospital
Bibliographic record
Abstract
BACKGROUND: Sepsis continues to take main stage in healthcare. Therefore, it remains crucial to elucidate contributors to sepsis mortality. The aim of this study is to determine the impact of race, insurance type, and code status on sepsis mortality in a community health system. METHODS: We conducted a retrospective cohort study of inpatient adults of any sex, race, and insurance type with a diagnosis of sepsis, severe sepsis, septic shock, or pneumonia. RESULTS: We included 913 patients, with an average age of 69 years for expired patients and 62 years for non-expiring patients (P < 0.0001). After controlling for other variables, patients who presented as comfort care arrest were 4.3 (95% confidence interval (CI): 1.8 to 9.9, P = 0.0007) times more likely to have died than full code patients. Those who were comfort care only were 10.6 (95% CI: 0.8 to 140.6, P = 0.0741) times more likely to have died than the full code, although this was not statistically significant. CONCLUSIONS: The results suggest that patients who are comfort care arrest have an increased risk of sepsis mortality. The results show no impact of insurance type or race on sepsis mortality, which is in contrast to some existing literature. The study suggests that institutions may need to investigate internal variables related to sepsis mortality.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".