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Record W4298271592 · doi:10.17615/tes0-eb72

What Are Effective Program Characteristics of Self-Management Interventions in Patients With Heart Failure? An Individual Patient Data Meta-analysis

2020· article· en· W4298271592 on OpenAlexfundno aff
Marcia Leventhal, Haruka Otsu, Ross T. Tsuyuki, Manuel Anguita, Arno W. Hoes, Javier Muñiz, Rolf H. H. Groenwold, Paul L. Hebert, Jaap C.A. Trappenburg, Darren A. DeWalt, Nini H. Jonkman, Susanna Ågren, Michele Heisler, Tiny Jaarsma, Gertrudis I. J. M. Kempen, Bárbara Riegel, Jan Mårtensson, Anna Strömberg, Lynda Blue, Heleen Westland, Frank Peters-Klimm, Dirk J. Lok, Pieta W.F. Bruggink‐André de la Porte, Michael W. Rich, Marieke J. Schuurmans

Bibliographic record

VenueDigital Commons@Becker (Washington University School of Medicine) · 2020
Typearticle
Languageen
FieldMedicine
TopicHeart Failure Treatment and Management
Canadian institutionsnot available
FundersNational Institutes of HealthAstraZeneca CanadaMerck CanadaZonMwMinisterie van Volksgezondheid, Welzijn en SportAstraZeneca
KeywordsMeta-analysisPsychological interventionHeart failureSelf-managementMedicineComputer scienceInternal medicineNursingArtificial intelligence

Abstract

fetched live from OpenAlex

To identify those characteristics of self-management interventions in patients with heart failure (HF) that are effective in influencing health-related quality of life, mortality, and hospitalizations.

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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.065
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0010.001
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 head0.050
GPT teacher head0.276
Teacher spread0.226 · 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 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

Citations0
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueDigital Commons@Becker (Washington University School of Medicine)Same topicHeart Failure Treatment and ManagementFrench-language works237,207