The role of objective cognitive dysfunction in subjective cognitive complaints after stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Objective cognitive performance (OCP) is often impaired in patients post-stroke but the consequences of OCP for patient-reported subjective cognitive complaints (SCC) are poorly understood. We performed a detailed analysis on the association between post-stroke OCP and SCC. METHODS: Assessments of OCP and SCC were obtained in 208 patients 3 months after stroke. OCP was evaluated using conventional and ecologically valid neuropsychological tests. Levels of SCC were measured using the CheckList for Cognitive and Emotional (CLCE) consequences following stroke inventory. Multivariate hierarchical regression analyses were used to evaluate the association of OCP with CLCE scores adjusting for age, sex and intelligence quotient. Analyses were performed to examine the global extent of OCP dysfunction (based on the total number of impaired neuropsychological tests, i.e. objective cognitive impairment index) and for each OCP test separately using the raw neuropsychological (sub)test scores. RESULTS: The objective cognitive impairment index for global OCP was positively correlated with the CLCE score (Spearman's rho = 0.22, P = 0.003), which remained significant in multivariate adjusted models (β = 0.25, P = 0.01). Results for the separate neuropsychological tests indicated that only one task (the ecologically valid Rivermead Behavioural Memory Test) was independently associated with the CLCE in multivariate adjusted models (β = -0.34, P < 0.001). CONCLUSIONS: Objective neuropsychological test performance, as measured by the global dysfunction index or an ecologically valid memory task, was associated with SCC. These data suggest that cumulative deficits in multiple cognitive domains contribute to subjectively experienced poor cognitive abilities in daily life in patients post-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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".