The interaction between geriatric and neighborhood vulnerability: Delineating prehospital risk among older adult emergency general surgery patients
Bibliographic record
Abstract
BACKGROUND: When presenting for emergency general surgery (EGS) care, older adults frequently experience increased risk of adverse outcomes owing to factors related to age ("geriatric vulnerability") and the social determinants of health unique to the places in which they live ("neighborhood vulnerability"). Little is known about how such factors collectively influence adverse outcomes. We sought to explore how the interaction between geriatric and neighborhood vulnerability influences EGS outcomes among older adults. METHODS: Older adults, 65 years or older, hospitalized with an AAST-defined EGS condition were identified in the 2016 to 2019, 2021 Florida State Inpatient Database. Latent variable models combined the influence of patient age, multimorbidity, and Hospital Frailty Risk Score into a single metric of "geriatric vulnerability." Variations in geriatric vulnerability were then compared across differences in "neighborhood vulnerability" as measured by variations in Area Deprivation Index, Social Vulnerability Index, and their corresponding subthemes (e.g., access to transportation). RESULTS: A total of 448,968 older adults were included. For patients living in the least vulnerable neighborhoods, increasing geriatric vulnerability resulted in up to six times greater risk of death (30-day risk-adjusted hazards ratio [HR], 6.32; 95% confidence interval [CI], 4.49-8.89). The effect was more than doubled among patients living in the most vulnerable neighborhoods, where increasing geriatric vulnerability resulted in up to 15 times greater risk of death (30-day risk-adjusted HR, 15.12; 95% CI, 12.57-18.19). When restricted to racial/ethnic minority patients, the multiplicative effect was four-times as high, resulting in corresponding 30-day HRs for mortality of 11.53 (95% CI, 4.51-29.44) versus 40.67 (95% CI, 22.73-72.78). Similar patterns were seen for death within 365 days. CONCLUSION: Both geriatric and neighborhood vulnerability have been shown to affect prehospital risk among older patients. The results of this study build on that work, presenting the first in-depth look at the powerful multiplicative interaction between these two factors. The results show that where a patient resides can fundamentally alter expected outcomes for EGS care such that otherwise less vulnerable patients become functionally equivalent to those who are, at baseline, more aged, more frail, and more sick. LEVEL OF EVIDENCE: Prognostic and Epidemiological; Level III.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".