Relevance of Cognition and Emotion for Patient-Reported Quality of Life After Stroke in Working Age: An Observational Cohort Study
Bibliographic record
Abstract
Background: Patient-reported quality of life (QoL) may help to capture sequela of stroke more comprehensively. We aimed to investigate QoL in working age persons with ischemic stroke regarding impaired domains and identify factors associated with better QoL. Methods: We invited persons with stroke aged 18-55 years to participate in this prospective observational study. We assessed QoL and self-rated health using the EuroQol 5 Dimension questionnaire (EQ-5D) during hospital stay (baseline) and at 3-months follow-up (FU). Additionally, the National Institute of Health Stroke Scale (NIHSS), modified Rankin Scale (mRS), cognition (Montreal Cognitive assessment, MOCA), emotion (Hospital Anxiety and Depression Scale), and return to work were evaluated. We used hierarchical regression to identify predictors of QoL (self-rated health and QoL Index score) at FU. Results: We included 138 persons with stroke (mean age = 43.6 ± 10 years; 41% female; median admission NIHSS = 2), of whom 99 participated at FU. QoL Index and self-rated health were correlated with NIHSS, mRS, anxiety, and depression at both timepoints. Although 80% had favorable functional outcome at FU (mRS < 2), high proportions of these persons reported problems in the "Pain and/or Discomfort" (25.3%) and "Anxiety/Depression" (22.8%) dimensions. Only discharge NIHSS and baseline MOCA independently predicted self-rated health at FU. Female sex, higher discharge NIHSS, and higher baseline depression scores predicted worse QoL Index scores at FU. Conclusions: Three months post-stroke, working age persons with stroke frequently reported problems in dimensions not assessed by the routinely used mRS. Despite correlations between clinical scales and QoL, patient-reported outcomes and screening for cognition and emotion ensure a more comprehensive assessment of post-stroke consequences relevant for QoL.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".