COGNITIVE PREDICTORS OF MEMORY IMPAIRMENT AFTER STROKE
Bibliographic record
Abstract
Background and Aims:Memory impairments are common after stroke and can have significant functional and prognostic implications, but the underlying mechanisms are not well understood. We hypothesized that recall and recognition memory after stroke will be differentially influenced by impairments in other cognitive domains, including executive functions and processing speed. Methods:We retrospectively analysed the performance of 198 stroke patients on the 5-word memory subtest of the MoCA (MoCA-Mem). Patients recalled the words without prompts (u2018free-recallu2019) and then with multiple-choice (u2018recognitionu2019). Using multiple linear regression, we investigated the association between MoCA-Mem performance and performance on 1) the other MoCA subtests, and 2) neuropsychology assessment of executive functions, processing speed and language. We also examined the positive and negative predictive value (PPV and NPV) of the MoCA-Mem for predicting impairment based on neuropsychology assessment of memory. Results:Of the 198 patients, 89% failed on the MoCA-Mem free-recall while 30% failed on recognition. Of the other MoCA subtests, performance on the executive domain was the only significant predictor of free-recall, while none were predictive of recognition. On neuropsychology assessment, executive function and processing speed was predictive of free-recall while language impairment was predictive of recognition. Using performance on neuropsychology assessment as the criteria, MoCA-Mem free-recall had good NPV (90.91%) but very poor PPV (42.68%) while recognition had both moderate NPV (70.83%) and PPV (71.43%).Conclusions:Recall and recognition memory impairment after stroke likely arise from separable cognitive processes. Our findings suggest that recognition memory tests may be more useful when assessing memory impairment 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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".