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Record W4410189665 · doi:10.15420/japsc.2024.37

Heart Failure in Sub-Saharan Africa: Current and Future Systems of Care

2025· article· en· W4410189665 on OpenAlexfundno aff
Victor M. Wauye, Dzifa Ahadzi, Krishna Udayakumar, G. Titus K. Ngeno

Bibliographic record

VenueJournal of Asian Pacific Society of Cardiology · 2025
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health and Epidemiology
Canadian institutionsnot available
FundersBeijing Friendship Hospital, Capital Medical UniversityAstellas PharmaConrad N. Hilton FoundationWorld Health OrganizationGrand Challenges CanadaAstraZenecaPfizerBill and Melinda Gates FoundationAmgenPfizer FoundationRockefeller FoundationDalio FoundationUnited States Agency for International Development
KeywordsCurrent (fluid)Heart failureBusinessMedicineCardiologyEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

Sub-Saharan Africa is undergoing rapid demographic and epidemiological transitions, fuelled by urbanisation, lifestyle changes and ageing populations. Consequently, the continent is faced with a ballooning burden of both communicable and non-communicable diseases (NCDs). Cardiovascular diseases are the leading cause of NCD-related mortality in SSA, with heart failure (HF) being the common phenotypic manifestation, afflicting a relatively younger population compared to other world regions. Even though the burden of HF is expected to double by 2030, HF systems of care remain poor in sub-Saharan Africa. Poor outcomes are especially aggravated by systemic barriers including under-resourced and siloed prevention, diagnostic, treatment and research efforts. Integrating HF care delivery through a systems approach and addressing risk factor prevention, screening and treatment across various tiers of care is crucial in abating the increasing burden of HF and NCDs. Further, a more patient-centred system of care that strengthens health financing, policies and system capabilities should be adopted to improve HF care and outcomes in sub-Saharan Africa.

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)0.002
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.511
Threshold uncertainty score0.522

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.029
GPT teacher head0.374
Teacher spread0.345 · 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 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

Citations2
Published2025
Admission routes1
Has abstractyes

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