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Record W2461732429 · doi:10.1177/1077558716655451

Improving Heart Failure Outcomes in Ambulatory and Community Care: A Scoping Study

2016· review· en· W2461732429 on OpenAlexafffund
Lorraine Jensen, Sarah M. Troster, Kimberly Cai, Avram Shack, Ying-Ju Chang, Wei Yun Wang, Ji Soo Kim, Diva Turial, Arlene S. Bierman

Bibliographic record

VenueMedical Care Research and Review · 2016
Typereview
Languageen
FieldMedicine
TopicHeart Failure Treatment and Management
Canadian institutionsSt. Michael's HospitalHealth Sciences CentreSunnybrook Health Science CentreDalhousie UniversityUniversity of TorontoNiagara Health System
FundersOntario Ministry of Health and Long-Term Care
KeywordsPsychological interventioneHealthMedicineAmbulatory careHealth careFidelityMEDLINESystematic reviewGerontologyNursing

Abstract

fetched live from OpenAlex

Despite a large body of literature testing interventions to improve heart failure care, care is often suboptimal. This scoping study assesses organizational interventions to improve heart failure outcomes in ambulatory settings. Fifty-two studies and systematic reviews assessing multicomponent, self-management support, and eHealth interventions were included. Studies dating from the 1990s demonstrated that multicomponent interventions could reduce hospitalizations, readmissions, mortality, and costs and improve quality of life. Self-management support appeared more effective when included in multicomponent interventions. The independent contribution of eHealth interventions remains unclear. No studies addressed management of comorbidities, geriatric syndromes, frailty, or end of life care. Few studies addressed risk stratification or vulnerable populations. Limited reporting about intervention components, implementation methods, and fidelity presents challenges in adapting this literature to scale interventions. The use of standardized reporting guidelines and study designs that produce more contextual evidence would better enable application of this work in health system redesign.

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)0.005
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.673
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.003
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 head0.134
GPT teacher head0.488
Teacher spread0.355 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations14
Published2016
Admission routes2
Has abstractyes

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