Abstract WP206: Cognitive Functioning Predicts Engagement in Inpatient Stroke Rehabilitation
Bibliographic record
Abstract
Introduction: Patient engagement during inpatient stroke rehabilitation (ISR) is critical to long-term outcomes. Cognitive deficits have demonstrated impact on engagement in rehabilitation. Here, we prospectively investigated the relationship between specific cognitive domains and patient engagement during ISR. Methods: Of 423 patients completing ISR, 127 (30%) had complete data with mean age=67.63 + 15.46 years, NIHSS=6.78 + 5.68, and onset from stroke to ISR admission=8.55 + 7.72 days. The sample comprised 55% males and 56.7% had a college education or more. The National Institute of Neurologic Disorders - Canadian Stroke Network (NINDS-CSN) 30-minute cognitive screening battery was administered within 72 hours of ISR admission to assess verbal fluency, executive functioning, and memory. The Hopkins Rehabilitation Engagement Ratings Scale (HRERS; total score 0-30, higher=greater engagement) was completed by treating therapists at ISR discharge. Spearman rank-order correlations (r s ) examined the relationships between the HRERS total score and the NINDS-CSN total (the mean z-score across subtests) as well as its 8 subtests. Items with correlations p<.10 were entered into a logistic regression (controlling for age, comorbidity, and stroke severity) to predict low (HRERS ≤ 25) versus high engagers (HRERS > 26). Results: NINDS-CSN total and 6 subtests assessing verbal fluency and executive function were weakly to moderately correlated with HRERS scores (r s =0.23-.38, all p’s <.01). Memory subtests were not associated with HRERS. Higher NINDS-CSN total score and subtests reflecting executive functions modestly increased the odds of being a high engager (Odds Ratios ranged from 1.03-1.08, 95% CIs ranged from 1.013-1.134, all p’s < .01). Conclusion: Poor executive functioning may pose a barrier to patient engagement in ISR. Executive functions may impact patients’ ability to shift among activities, maintain attention, and rapidly process information during therapy. Rehabilitation therapists should consider making environmental modifications, providing more frequent guidance and positive reinforcement, and presenting simplified material to increase engagement in stroke patients with executive dysfunction.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".