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Record W4408535092 · doi:10.1016/j.ajmo.2025.100097

Sex Specific Outcomes With Cardiac Resynchronization Therapy in Patients With Symptomatic Heart Failure Having Reduced Left Ventricular Ejection Fraction: A Systematic Review and Meta-Analysis

2025· review· en· W4408535092 on OpenAlexaboutno aff
Muhammad Hamayal, Mustansar Abbas, Momina Hafeez, Saira Mahmud, Warda Shahid, Saman Naeem, Muhammad Danyal Tahir, Abdullah Al Abbas, Iqra Iftikhar

Bibliographic record

VenueAmerican Journal of Medicine Open · 2025
Typereview
Languageen
FieldMedicine
TopicCardiac pacing and defibrillation studies
Canadian institutionsnot available
Fundersnot available
KeywordsEjection fractionCardiac resynchronization therapyHeart failureCardiologyInternal medicineMedicineMeta-analysis

Abstract

fetched live from OpenAlex

Cardiac resynchronization therapy (CRT) has emerged instrumental in managing heart failure. Notably, there is a lack of evidence of CRT efficacy among both sexes. Thus, this meta-analysis focuses on the long-term benefits of CRT in both sexes. PubMed, The Cochrane Library and clinicaltrials.gov were searched for articles from 2010 to 2024. ROB2 was used to assess risk of bias of RCTs. Newcastle Ottawa Scale was used for quality appraisal of cohorts. Meta-analysis was conducted on Revman 5.4. Out of 2722 articles, only 9 RCTs and 18 cohorts were included. Our results demonstrated that females had a significantly lower risk of composite outcomes compared to males in both RCTs (RR 0.80; 95% CI [0.68, 0.94], P = .006) and cohorts (RR 0.76; 95% CI [0.63, 0.92], P = .004). Results were similar for all-cause mortality. For heart failure hospitalization, only cohorts showed a significant lesser risk in females (RR 0.78; 95% CI [0.65, 0.93], P = .006). Left ventricular ejection fraction improved significantly in females but no differences were observed for NYHA class improvement. Males showed a 31% lower survival rate. However future trials are needed to highlight this variation.

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 categoriesMeta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.268
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0160.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.031
GPT teacher head0.345
Teacher spread0.314 · 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 designMeta-analysis
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

Citations0
Published2025
Admission routes1
Has abstractyes

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