Working memory in mild cognitive impairment and Alzheimer's disease: Contribution of forgetting and predictive value of complex span tasks.
Bibliographic record
Abstract
OBJECTIVE: This study examines working memory (WM) in mild cognitive impairment (MCI) and Alzheimer's disease (AD). METHOD: Performances on sentence span and operation span were measured in individuals meeting criteria for MCI (n = 20) and AD (n = 16) as well as in healthy older adults (n = 20). In addition, the effect of retention interval was assessed by manipulating the length of first and last items of trials (long-short vs. short-long), as forgetting might contribute to impaired performance in AD and MCI. RESULTS: Results show a group effect (p < .001, η² = .47): In both conditions and for both material types, WM span is lower in AD than in MCI (p < .001), which in turn is lower than in healthy aging (p < .05). An effect of retention interval on complex span was found for all groups (p < .001, η² = .57), supporting a role for forgetting within WM. When computing a proportional interval effect (p < .05, η² = .12), it was found that persons with AD were more sensitive to retention interval than were healthy older adults (p < .05). Among persons with MCI, those who later showed significant clinical deterioration or progression to AD were more affected by retention interval (p < .05, η² = .28) than were those who remained stable. Furthermore, deficits in AD are associated with a higher proportion of intrusion errors, particularly those from the current trial (p < .05, η² = .15), which could reflect inhibitory processes. CONCLUSIONS: Overall, these results indicate impaired WM in age-related disorders with a gradient between MCI and AD. Retention interval increases deficit in persons with AD. It also shows potential in predicting a negative prognosis in those with MCI.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".