Neurocognitive markers of cognitive impairment: Exploring the roles of speed and inconsistency.
Bibliographic record
Abstract
A well-known challenge for research in the cognitive neuropsychology of aging is to distinguish between the deficits and changes associated with normal aging and those indicative of early cognitive impairment. In a series of 2 studies, the authors explored whether 2 neurocognitive markers, speed (mean level) and inconsistency (intraindividual variability), distinguished between age groups (64-73 and 74-90+ years) and cognitive status groups (nonimpaired, mildly impaired, and moderately impaired). Study 1 (n = 416) showed that both level and inconsistency distinguished between the age and 2 cognitive status (not impaired, mildly impaired) groups, with a modest tendency for inconsistency to predict group membership over and above mean level. Study 2 (n = 304) replicated these results but extended them because of the qualifying effects associated with the unique moderately impaired oldest group. Specifically, not only were the groups more firmly distinguished by both indicators of speed, but evidence for the differential contribution of performance inconsistency was stronger. Neurocognitive markers of speed and inconsistency may be leading indicators of emerging cognitive impairment.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".