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Hospital at Home as a Treatment Strategy for Worsening Heart Failure

2023· article· en· W4386271747 on OpenAlexaff
Hubert B. Haywood, Gregg C. Fonarow, Muhammad Shahzeb Khan, Harriette G.C. Van Spall, Alanna A. Morris, Michael E. Nassif, M. Kittleson, Javed Butler, Stephen J. Greene

Bibliographic record

VenueCirculation Heart Failure · 2023
Typearticle
Languageen
FieldMedicine
TopicHeart Failure Treatment and Management
Canadian institutionsMcMaster UniversityPopulation Health Research Institute
Fundersnot available
KeywordsMedicineWaiverHeart failureAmbulatory careInpatient careIntensive care medicineAmbulatoryDisease managementEmergency medicineMedical emergencyHealth careDiseaseInternal medicine

Abstract

fetched live from OpenAlex

Hospital at home (HaH) is an innovative care model that may be particularly suited for heart failure (HF). Outpatient visits and inpatient care have been the 2 traditional settings for HF care, yet may not match the social and medical needs of patients at all times. Alternative models such as HaH may represent an effective and patient-centered option for select patients with worsening HF. To date, limited research in HF and other disease states has supported HaH as being safe and lower cost than traditional inpatient admission. Supporting HaH are new payment structures, such as Medicare's Acute Hospital Care at Home waiver program. In combination with outpatient visits, outpatient intravenous diuretic clinics, inpatient care, and cardiac intensive care, HaH could be a core component of a comprehensive care model with the potential to match resource utilization with the needs of patients across the spectrum of HF severity, and improve patient outcomes.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.152
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.002

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 head0.031
GPT teacher head0.301
Teacher spread0.270 · 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.

Study designNot applicable
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

Citations19
Published2023
Admission routes1
Has abstractyes

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