Neurocognitive markers of cognitive impairment: Exploring the roles of speed and inconsistency.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it