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Resource Use in the Last 6 Months of Life Among Patients With Heart Failure in Canada

2010· article· en· W2126616096 on OpenAlexafffundabout
Padma Kaul, Finlay A. McAlister, Justin A. Ezekowitz, Jeffrey A. Bakal, Lesley H. Curtis, Hude Quan, Merril L. Knudtson, Paul W. Armstrong

Bibliographic record

VenueArchives of Internal Medicine · 2010
Typearticle
Languageen
FieldMedicine
TopicHeart Failure Treatment and Management
Canadian institutionsUniversity of Alberta
FundersCanadian Institutes of Health Research
KeywordsMedicineEmergency departmentHeart failureEmergency medicineComorbidityPopulationIndirect costsMultivariate analysisHealth careOutpatient visitsInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

BACKGROUND: Heart failure (HF) is a debilitating and chronic condition associated with significant morbidity and mortality. However, much less is known about end-of-life (EOL) costs among patients with HF. METHODS: To examine trends in resource use and costs during the last 6 months of life among elderly patients with HF, we evaluated data regarding all patients 65 years or older with HF who died between January 1, 2000, to December 31, 2006, in Alberta, Canada, and examined costs associated with all-cause hospitalizations, intensive care, emergency department visits, outpatient visits, physician office visits, and outpatient drugs in the 180 days before death. Overall costs and predictors of costs to the health care system were also examined. RESULTS: The study population included 33,144 patients with HF who died. The mean age at death was 83 years. The clinical profile of patients changed during the study period, with an increasing comorbidity burden over time. Between 2000 and 2006, the percentage of patients hospitalized during the last 6 months of life decreased from 84% to 76% (P<.01); and the percentage dying in hospital decreased from 60% to 54% (P<.01). In 2006, the average EOL cost was $27,983 in Canadian dollars. In multivariate analyses, increasing age was inversely associated with EOL costs and comorbid conditions were associated with higher costs. CONCLUSIONS: Resource use in the last 6 months of life among patients with HF in Alberta is changing, with a reduction in hospitalizations, in-hospital deaths, and an increase in the use of outpatient services. However, EOL costs are substantial and continue to increase.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.291
Threshold uncertainty score0.338

Codex and Gemma teacher scores by category

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

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 head0.006
GPT teacher head0.209
Teacher spread0.203 · 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 teacher head, 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

Citations75
Published2010
Admission routes3
Has abstractyes

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