P.114 The impact of pre-stroke frailty on stroke rehabilitation outcomes: a retrospective cohort study
Bibliographic record
Abstract
Background: Stroke is a leading cause of disability worldwide, resulting in long-term impairments requiring rehabilitation. Frailty, characterized by reduced physiological reserve and vulnerability to stressors, is associated with poor health outcomes. When assessed using the Clinical Frailty Scale (CFS), frailty has been linked to adverse outcomes; however, its role in stroke rehabilitation remains underexplored. This study investigates the impact of pre-stroke frailty on functional recovery during inpatient stroke rehabilitation. Methods: A retrospective cohort study was conducted on 206 stroke patients admitted between 2020-2022. Pre-stroke frailty was assessed using the CFS, and rehabilitation outcomes were measured using Functional Independence Measure (FIM) gain and efficiency. Differences across CFS categories, stroke location, age, and sex were statistically analyzed. Results: Among these patients, 42.7% were female, and 75.7% were aged 60 or above. There were no significant differences in FIM gain or efficiency across CFS categories (p > 0.05). Frailty was associated with lower admission (p = 0.041) and discharge (p = 0.032) FIM scores. Conclusions: Pre-stroke frailty, assessed retrospectively using the CFS, does not predict functional improvement or efficiency during inpatient rehabilitation. However, frailty was associated with poorer functional status at admission and discharge. All patients meeting the admission criteria benefited from rehabilitation, regardless of frailty level.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".