Task switching performance reveals heterogeneity amongst patients with mild cognitive impairment.
Bibliographic record
Abstract
OBJECTIVE: To assess executive function in patients with mild cognitive impairment (MCI) and to determine whether task switching ability is associated with transition to Alzheimer's disease. METHODS: Twenty-seven MCI patients and 19 older controls were tested using a cued letter-digit classification switching task. Sixteen patients could perform the task (MCI-able), 6 could not (MCI-unable), and 5 were able only with cognitive support (MCI-cue). Demographic, neuropsychological, event-related potential (ERP), MRI, and genetic data were also collected. RESULTS: The four groups did not differ on age, gender, and APO E4 frequency. Compared to the controls, the MCI-unable group had significantly poorer performance on the Trail Making task (η2 = .430), lower education (η2 = .234), and smaller cortical volume (η2 = .245). Most MCI patients exhibited task-switching deficits but to vastly different degrees and with varying outcomes. The combined pattern of neuropsychological and task switching performance indicates that the MCI-able patients displayed memory retrieval difficulties (F(2,39) = 3.6, p = .036, MSE = 1.44), generally preserved task switching abilities, and had a high probability of remaining dementia-free at follow-up. The MCI-cue patients had increased mixing costs, F(2,39) = 11.0, p < .001, MSE = .07; the MCI-unable patients showed episodic memory deficits, and both groups had a high probability of poor outcome (i.e., developing AD or dying within four years). CONCLUSION: This study demonstrates that variability in performance on measures of task-switching can highlight important heterogeneity in the MCI population.
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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 | 0.000 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".