Very Early Cognitive Screening and Self-Reported Feeling of Fatigue Three Months After Stroke
Bibliographic record
Abstract
Stroke is a major cause of disability and the second leading cause of death worldwide. Post-stroke fatigue has been reported as one of the most limiting symptoms after a stroke. Early identification of risk factors for developing post-stroke fatigue is important for providing timely rehabilitation. A correlation has been found between fatigue and cognitive impairment after stroke, but 2 months after stroke at the earliest. In the present study, we examined whether cognitive function screening using the Montreal Cognitive Assessment (MoCA) very early after stroke could explain fatigue 3 months after stroke. A total of 311 stroke patients admitted to a comprehensive stroke unit in Sweden between 2011 and 2016 were included in this longitudinal study. Cognition was screened within 2 days after admission to the stroke unit. Data on self-reported feeling of fatigue were retrieved from Riksstroke's 3-month follow-up form. The data were analyzed using binary logistic regression. We found that the cognitive function in an acute phase after stroke could not explain self-reported feeling of fatigue in a later stage. The correlation between cognitive impairment and fatigue that has been reported may be detectable no earlier than the subacute phase of stroke. As previous studies have shown that functional outcome, severity of stroke, and sex also correlate with fatigue after stroke, we controlled for these variables in our analysis. In line with previous studies, we found that female patients had higher odds of experiencing fatigue. This is something that health care professionals should be aware of when working with stroke patients.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".