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Record W3046691819 · doi:10.1002/alz.12094

Health‐care use and cost for multimorbid persons with dementia in the National Health and Aging Trends Study

2020· article· en· W3046691819 on OpenAlexaff
Janet L. MacNeil Vroomen, Mary Thompson, Linda Leo‐Summers, Richard A. Marottoli, Heather Allore

Bibliographic record

VenueAlzheimer s & Dementia · 2020
Typearticle
Languageen
FieldMedicine
TopicChronic Disease Management Strategies
Canadian institutionsUniversity of Waterloo
FundersNational Institute on Aging
KeywordsMedicineDementiaKidney diseaseDepression (economics)Atrial fibrillationSpecialtyDiseaseMedical costsAmbulatoryStroke (engine)Health careIndirect costsAmbulatory careIntensive care medicineGerontologyEmergency medicineInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Most persons with dementia have multiple chronic conditions; however, it is unclear whether co-existing chronic conditions contribute to health-care use and cost. METHODS: Persons with dementia and ≥2 chronic conditions using the National Health and Aging Trends Study and Medicare claims data, 2011 to 2014. RESULTS: Chronic kidney disease and ischemic heart disease were significantly associated with increased adjusted risk ratios of annual hospitalizations, hospitalization costs, and direct medical costs. Depression, hypertension, and stroke or transient ischemic attack were associated with direct medical and societal costs, while atrial fibrillation was associated with increased hospital and direct medical costs. No chronic condition was associated with informal care costs. CONCLUSIONS: Among older adults with dementia, proactive and ambulatory care that includes informal caregivers along with primary and specialty providers, may offer promise to decrease use and costs for chronic kidney disease, ischemic heart disease, atrial fibrillation, depression, and hypertension.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.054
Threshold uncertainty score0.107

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.112
GPT teacher head0.375
Teacher spread0.263 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations28
Published2020
Admission routes1
Has abstractyes

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