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Record W4409283007 · doi:10.1093/eurjpc/zwaf200

Reply to: enhancing cardiac rehabilitation: addressing multidimensional aspects of frailty

2025· article· en· W4409283007 on OpenAlexaff
D. Scott Kehler, Evan MacEachern, Nicholas Giacomantonio

Bibliographic record

VenueEuropean Journal of Preventive Cardiology · 2025
Typearticle
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsDalhousie University
Fundersnot available
KeywordsMedicineRehabilitationGerontologyIntensive care medicinePhysical medicine and rehabilitationPhysical therapy

Abstract

fetched live from OpenAlex

We appreciate the interest by Zhang et al.1 related to our systematic review and meta-analysis on cardiac rehabilitation (CR) and frailty.2 The commentary related to CR delivery models, psychological well-being, and cognitive and social frailties add perspectives that can further advance the field. Here, we acknowledge and build upon their thoughtful comments. Zhang et al.1 propose the study of the heterogeneity of CR program delivery models to address frailty. The emergence of digital and home-based CR programs presents a promising opportunity to improve accessibility and adherence, particularly among frail individuals and those in underserved areas. Notably, only two studies in our review included a virtual CR or home-based program. One study included in our review found that the overall effect of centre- and virtual-based CR programs did not change; however, a mild–moderate frailty levels at CR admission, only the virtually delivered program improved frailty at discharge.3 Therefore, we also support further research comparing the efficacy of traditional, home-based, and digital CR models in frail populations to identify the most effective strategies for improving patient outcomes.

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)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.797
Threshold uncertainty score0.606

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
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 head0.023
GPT teacher head0.314
Teacher spread0.291 · 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 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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