Return to work after young stroke: A systematic review
Bibliographic record
Abstract
Background The incidence of stroke in young adults is increasing. While many young survivors are able to achieve a good physical recovery, subtle dysfunction in other domains, such as cognition, often persists, and could affect return to work. However, reported estimates of return to work and factors affecting vocational outcome post-stroke vary greatly. Aims The aims of this systematic review were to determine the frequency of return to work at different time points after stroke and identify predictors of return to work. Summary of review Two electronic databases (Medline and Embase) were systematically searched for articles according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. A total of 6473 records were screened, 68 were assessed for eligibility, and 29 met all inclusion criteria (working-age adults with stroke, return to work evaluated as an outcome, follow-up duration reported, and publication within the past 20 years). Return to work increased with time, with median frequency increasing from 41% between 0 and 6 months, 53% at 1 year, 56% at 1.5 years to 66% between 2 and 4 years post-stroke. Greater independence in activities of daily living, fewer neurological deficits, and better cognitive ability were the most common predictors of return to work. Conclusion This review highlights the need to examine return to work in relation to time from stroke and assess cognition in working age and young stroke survivors. The full range of factors affecting return to work has not yet been explored and further evaluations of return to work interventions are warranted.
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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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".