Language And Cognitive Tasks Most Predictive Of Mild Cognitive Impairment
Bibliographic record
Abstract
Mild cognitive impairment (MCI) is characterized by a decline in cognition greater than expected given age and education level. Multiple screening instruments aim to detect subtle cognitive deficits associated with MCI. However, there are inconsistencies in the sensitivity and specificity of the instruments and tasks most reliable for identification of MCI. The present study aims to identify which tasks, task combinations and/or question items best discriminate MCI from healthy older adults (HOA). Ten participants with ages ranging from 55 to 82 were administered the Montreal Cognitive Assessment (MoCA), the Mini Mental State Examination (MMSE), and Arizona Battery for Communication Disorders (ABCD. Results revealed the MoCA accurately screened for MCI in three out of four participants. However, the MoCA misdiagnosed two HOA. While individuals with MCI consistently scored lower than HOA on the MMSE, all ten participants scored within normal limits. Analysis of the findings revealed the subtests from the ABCD with the greatest sensitivity for identifying MCI included: repetition, reading comprehension- sentences, mental status, story retelling-immediate, generative naming, and confrontation naming.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| 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".