Prevalence, Impact, and Treatment of Co-Occurring Osteoarthritis in Patients With Stroke Undergoing Rehabilitation
Bibliographic record
Abstract
Background and Purpose: Early, frequent rehabilitation is an important factor for optimizing stroke recovery outcomes. Medical comorbidities, such as osteoarthritis, that affect the ability to participate in rehabilitation could therefore have a detrimental impact on such outcomes. Both stroke and osteoarthritis are becoming more common in developed nations as the population ages. First-line osteoarthritis treatments, such as oral nonsteroidal anti-inflammatory drugs, are often avoided poststroke due to interaction with secondary prevention stroke risk-factor management. Our objective was to summarize the current literature concerning co-occurring osteoarthritis and stroke prevalence, its functional impact, and treatment options. Methods: Narrative review using a comprehensive literature search of PubMed, osteoarthritis, and stroke guidelines. Outcomes related to co-occurrence prevalence, osteoarthritis as a stroke risk-factor, osteoarthritis-related imaging and treatment were extracted and summarized descriptively. Overall quality of the evidence was summarized using Grading of Recommendations Assessment, Development and Evaluation. Results: We identified 23 studies and guidelines related to our objective. Overall quality of the evidence was very low. Conclusions: Few trials have investigated the relationship between osteoarthritis and stroke, nor osteoarthritis-specific pain and function management for stroke survivors. High-quality research evaluating the impact of osteoarthritis on stroke rehabilitation is needed.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".