Utilization Patterns and Clinical Factors Associated With Hospitalization in Early‐Stage Dementia With Lewy Bodies
Bibliographic record
Abstract
OBJECTIVES: Characterize patterns of hospitalization and emergency department (ED) visits in early-stage dementia with Lewy bodies (DLB). METHODS: We analyzed electronic health records and claims data from a U.S. healthcare system up to 3 years before/after initial diagnosis of DLB (n = 175), Alzheimer's disease (AD, n = 2478), or vascular dementia (VD, n = 513). Controls were randomly matched 3:1 with the DLB group on sex/age (n = 525). Generalized linear models were used to compare rates and types of utilization between diagnosis group with adjustment for patient characteristics. RESULTS: Patients with DLB had significantly greater rates of hospitalization and ED visits compared to patients with AD (Incidence Rate Ratio (IRR): 1.46, 95% CI 1.24, 1.73, IRR: 1.46, 95% CI 1.29, 1.77, respectively) and controls (IRR: 1.77, 95% CI 1.46, 2.14, IRR: 2.21, 95% CI 1.82, 2.69, respectively) and ED visits compared to those with VD (IRR: 1.24, 95% CI 1.03, 1.50). Patients with DLB were over 50% more likely to have a hospitalization associated with falls compared to those with AD and VD (OR: 1.75, 95% CI 1.16, 2.62 OR: 1.56, 95% CI: 1.01, 2.48, respectively). Compared to patients with AD, DLB patients were found to have 2.9-time higher likelihood of experiencing at least one hospitalization (Odds Ratio: 2.89. 95% CI: 1.17, 6.45). CONCLUSIONS: Patients with DLB were substantially more likely to utilize ED services than patients with AD, VD, or controls, and more likely to experience hospitalizations compared to AD and control groups. Fall prevention and psychiatric treatment may be particularly important in reducing hospitalizations in early-stage DLB.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".