Longitudinal Trajectories of Cognitive Function after Stroke: A Prospective Cohort Study
Bibliographic record
Abstract
Abstract Background The study aimed to determine longitudinal trajectories of cognitive function during the first year after stroke. MethodsThe Montreal Cognitive Assessment (MoCA) was used to screen cognitive function at 36-48 hours, 3-months, and 12-months post-stroke. Individuals who shared similar trajectories were classified by applying the group-based trajectory models. ResultsData from 94 patients were included in the analysis. Three cognitive functioning groups were identified by the trajectory models: high (14 patients [15%]), medium (58 [62%]) and low (22 [23%]). For the high and medium groups, cognitive function improved at 12 months, but this did not occur in the low group. After age, sex and education matching to normative MoCA form the Swedish population, 52 patients (55%) were found to be cognitively impaired at baseline, and few patients had recovered at 12 months. The impact on memory differs between cognitive functioning groups, whereas the impact on activities of daily live was not different.ConclusionsPatients with the poorest cognitive function did not improve at one-year poststroke and were prone to severe memory problems. These findings may help to increase focus on long-term rehabilitation plans for those patients, and to more accurately assess their needs and difficulties experienced in daily living.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".