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Record W4408837325 · doi:10.1016/j.ahr.2025.100227

Clinical profile of heart failure in geriatric patients in a tertiary cardiac hospital of Bangladesh

2025· article· en· W4408837325 on OpenAlexaff
Mahbuba Yesmin, Lutfun Nahar Nizhu, Afroja Alam, Abdullah Al Mamun

Bibliographic record

VenueAging and Health Research · 2025
Typearticle
Languageen
FieldMedicine
TopicHeart Failure Treatment and Management
Canadian institutionsSunnybrook Health Science Centre
Fundersnot available
KeywordsHeart failureTertiary careMedicineEmergency medicineIntensive care medicineInternal medicineCardiology

Abstract

fetched live from OpenAlex

This study included 100 geriatric patients admitted at National Institute of Cardiovascular Diseases (NICVD) in Bangladesh having clinical features of heart failure, possible other information (Framingham Criteria) and increase in natriuretic peptide or echocardiographic findings. It was a single center, cross sectional study. Regarding functional NYHA classification, 14 % presented with class I, 30 % with class II, 35 % presented with class III, 21 % presented with class IV heart failure. Regarding left ventricular ejection fraction (LVEF), heart failure (HF) was classified into the following categories: (1) HF with reduced EF (HFrEF) - 19 %; (2) HF with improved EF (HFmpEF) - 13 %; (3) HF with mildly reduced EF (HFmrEF) - 22 % and (4) HF with preserved EF (HFpEF) - 46 % . 23 % of the study population had iron deficiency anemia and about one-third-patient had associated renal failure (AKI/CKD). This study revealed that a significant geriatric population has unrecognized heart failure (HFpEF).

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.058
GPT teacher head0.442
Teacher spread0.385 · 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 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
Published2025
Admission routes1
Has abstractyes

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