Effect of Diabetes on Post-stroke Recovery: A Systematic Narrative Review
Bibliographic record
Abstract
Background: Patients with stroke often have comorbid diabetes. Considering its detrimental effects on brain function, diabetes may increase the risk of poor recovery. Methods: The aim of this review was to investigate the effect of diabetes on post-stroke recovery by a systematic review. Several specific aspects of post-stroke recovery, including activities of daily living (ADL), motor, cognitive, and quality of life (QOL) recovery, were examined. We searched the PubMed, SCOPUS, Embase, and Cochrane Library databases for relevant studies on the effect of diabetes on post-stroke recovery, published until May 26, 2021. A total of 52,051 potentially relevant articles were identified. After reading the titles and abstracts and assessing their eligibility based on full-text articles, 34 publications were included in this review. Results: Of 29 studies that assessed ADL recovery after stroke, 22 studies suggested that diabetes had a negative effect on recovery of ADL after stroke. Regarding motor recovery, only one out of four studies showed that diabetes had some effect on motor recovery after stroke. Of the two studies on cognitive recovery, one reported that diabetes was an independent predictor of poor cognitive recovery after stroke. Three studies on QOL reported that a poor QOL after stroke was associated with the presence of diabetes. Conclusions: The current review suggests that the post-stroke recovery of ADL seems to be poorer in patients with diabetes than patients without diabetes. Further, there are insufficient data to conclude the effect of diabetes on motor and cognitive recovery, but it may have some influence on the quality of life after stroke. Systematic Review Registration: doi: 10.37766/inplasy2021.11.0032 , identifier: INPLASY2021110032.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".