Dementia in older people admitted to hospital: An analysis of length of stay and associated costs
Bibliographic record
Abstract
OBJECTIVES: Patients with dementia in the acute setting are generally considered to impose higher costs on the health system compared to those without the disease largely due to longer length of stay (LOS). Many studies exploring the economic impact of the disease extrapolate estimates based on the costs of patients diagnosed using routinely collected hospital discharge data only. However, much dementia is undiagnosed, and therefore in limiting the analysis to this cohort, we believe that LOS and the associated costs of dementia may be overestimated. We examined LOS and associated costs in a cohort of patients specifically screened for dementia in the hospital setting. METHODS: Using primary data collected from a prospective observational study of patients aged ≥70 years, we conducted a comparative analysis of LOS and associated hospital costs for patients with and without a diagnosis of dementia. RESULTS: There was no significant difference in overall length of stay and total costs between those with (μ = 9.9 days, μ = € 8246) and without (μ = 8.25 days, μ = € 6855) dementia. Categorical data analysis of LOS and costs between the two groups provided mixed results. CONCLUSIONS: The results challenge the basis for estimating the costs of dementia in the acute setting using LOS data from only those patients with a formal dementia diagnosis identified by routinely collected hospital discharge data. Accurate disease prevalence data, encompassing all stages of disease severity, are required to enable an estimation of the true costs of dementia in the acute setting based on LOS.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".