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Record W3025736280 · doi:10.22374/cjgim.v15isp1.418

Heart Failure in the Young: The Patient Perspective and Lived-Experience

2020· article· en· W3025736280 on OpenAlexaffvenueabout
Thomas M. Roston, Marc Bains, Jillianne Code, Sean Virani

Bibliographic record

VenueCanadian Journal of General Internal Medicine · 2020
Typearticle
Languageen
FieldMedicine
TopicHeart Failure Treatment and Management
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicinePerspective (graphical)Lived experienceHeart failureDiseaseGerontologyIntensive care medicineCardiologyInternal medicinePsychotherapistPsychology

Abstract

fetched live from OpenAlex

Heart failure (HF) is an often-debilitating syndrome that carries a lifelong burden of increased morbidity and mortality. While most affected individuals are elderly with ischemic heart disease, there are subsets of younger individuals who will develop HF. In this group, non-ischemic causes of cardiomyopathy are more common, optimal therapies are less clear, and the personal and societal impact is often greater. The lived-experience of younger patients highlights several unmet needs not addressed by large HF trials that influence survival, personal and financial wellness and return to activities of daily living. In Canada, there is an increasing focus on the patient perspective, especially amongst young individuals, when devising guidelines, policies and promoting advocacy in HF. This article describes the lived-experience of HF through the case example of a young patient, summarizes the clinical challenges in this age-group, and discusses opportunities to elevate the patient experience of care as a performance indicator.

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)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0050.004
Scholarly communication0.0040.003
Open science0.0010.005
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.030
GPT teacher head0.284
Teacher spread0.254 · 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 designQualitative
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 routes3
Has abstractyes

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