A narrative review of the impact of medical comorbidities on stroke rehabilitation outcomes
Bibliographic record
Abstract
PURPOSE: Medical comorbidities in stroke patients influence acute mortality, but may also affect participation of survivors in rehabilitation. There is limited research investigating the impact of comorbidities on stroke rehabilitation outcomes. The review will explore the literature on the impact of comorbidities on stroke rehabilitation outcome. MATERIALS AND METHODS: The literature was searched systematically, including MEDLINE database, EMBASE and PsychINFO, combining variations of the terms stroke, rehabilitation and comorbidities. Results were limited to English language publications. Included studies had a functional outcome. RESULTS: Twenty relevant articles were identified. Fifteen small prospective or large retrospective studies using global comorbidity scales produced conflicting relationships between comorbidities and rehabilitation outcomes. Five publications addressed specific comorbidities, with three studies finding negative correlation between diabetes and rehabilitation outcomes, although effects diminished with age. In general, there were discrepancies in how comorbidities were identified. Few studies specifically focused on comorbidities and/or rehabilitation outcomes. CONCLUSIONS: There is conflicting evidence regarding the impact of comorbidities on stroke rehabilitation outcomes. However, the presence of more severe diabetes may be associated with worse outcomes. The role of comorbidities in stroke rehabilitation would be best clarified with a large cohort study, with precise comorbidity identification measured against rehabilitation specific outcomes. Implications for rehabilitation Benefit of rehabilitation after stroke in improving functional outcome is well-established. Many stroke patients have comorbid conditions which can impact rehabilitation participation, leading to less benefit obtained from rehabilitation. The burden of comorbid conditions may slow rehabilitation progress, which may warrant a longer duration of rehabilitation to obtain required functional gain to be discharged into the community.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".