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Record W4412091655 · doi:10.1080/00325481.2025.2530921

Analysis of frailty determinants in chronic stroke patients

2025· article· en· W4412091655 on OpenAlexaboutno aff
Ioan-Alexandru Chirap-Mitulschi, Sabina A Antoniu

Bibliographic record

VenuePostgraduate Medicine · 2025
Typearticle
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineStroke (engine)GerontologyPhysical medicine and rehabilitationPhysical therapy

Abstract

fetched live from OpenAlex

Objective Frailty is becoming more widely acknowledged as a critical factor that impacts the quality of life and health outcomes of patients with chronic conditions, including those who have experienced a stroke. This study aims to analyze the determinants of frailty in a prospective cohort of chronic stroke patients undergoing rehabilitation via relevant clinical, functional, and quality-of-life measures.Methods In this prospective study, 124 chronic stroke patients (mean age: 63.3 years, SD = 10.5) were assessed for frailty using the Edmonton Frailty Scale (EFS). Variables included age, stroke severity indices, functional status, and quality of life. Descriptive and inferential analyses was performed.Results The majority (81.5%) of patients had ischemic strokes. Frail patients were older (mean age: 64.6 vs. 55.2 years, p < 0.005), had more severe strokes (modified Rankin scale (mRS) 3.87 vs. 2.53, p < 0.005; National Institutes of Health Stroke Scale (NIHSS) 6.08 vs. 3.47, p < 0.005), greater functional impairment (Barthel Index 52.9 vs. 80.6, p < 0.005), and lower quality of life (2.78 vs. 4.02, p < 0.005). Logistic regression showed that advanced age and lower self-efficacy significantly predicted frailty (age: OR = 1.1, 95% CI: 1.01–1.21; Stroke Self-Efficacy Questionnaire (SSEQ): OR = 0.72, 95% CI: 0.55–0.95). The ROC analysis demonstrated that age had an AUC of 0.742 (95% CI: 0.65–0.86, p < 0.001), whereas the AUC for SSEQ was 0.924 (95% CI: 0.86–0.96, p < 0.001).Conclusions In patients with chronic stroke, frailty, as measured with the EFS, is best predicted by age and by the stroke-related impaired self-efficacy. Interestingly, the latter is a stronger frailty predictor, especially in younger patients. These findings indicate that both physiological and disease-related functional declines contribute to the development of frailty. However, additional longitudinal studies are necessary to validate the causal association and to account for potential confounding factors like depression or social support.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.025
GPT teacher head0.332
Teacher spread0.308 · 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".

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Citations1
Published2025
Admission routes1
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