Association of cognitive function very early after stroke with subjective cognitive complaints after 3 months, a register-based study
Bibliographic record
Abstract
OBJECTIVE: Cognitive deficits are commonly observed after stroke and have been associated with the cognitive decline and development of dementia in later stages. This study aimed to investigate whether cognition screened at acute stroke units could explain subjective cognitive complaints 3 months after stroke and evaluate how the severity of stroke and age could influence this association. METHODS: In this register-based longitudinal study, data were retrieved from three Swedish registers between November 2014 and June 2019. Information on subjective cognitive complaints (SCC) was collected from the Riksstroke 3-month follow-up form, which were used to analyze the primary outcomes. Cognitive function screened using the Montreal Cognitive Assessment (MoCA) at acute stroke units was expressed as the primary independent variable. RESULTS: Of the 1977 patients included in the study, 58% were males, the median age was 73 years, and 63% had a minor stroke. A total of 60% of patients had impaired cognition at acute stroke units (MoCA score, <26), of whom 40.3% reported at least 1 cognitive problem after 3 months. In adjusted binary regression analysis models, patients with normal cognitive function had lower odds for SCCs. This pattern was observed regardless of age and in patients with a minor stroke. CONCLUSIONS: Intact cognition early after stroke was related to decreased odds of subjective cognitive complaints at the 3-month follow-up. This study highlights the importance of both early cognitive screening after stroke and subjective cognitive complaints, which have been shown to be associated with cognitive decline. Furthermore, we suggest the importance of discussing cognitive function with patients during regular follow-up in primary care, usually 3 months after stroke.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| 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".