A path to early diagnosis of mild cognitive impairment and dementia: validity and reliability of the myMemCheck® self-administered screening tool
Bibliographic record
Abstract
BACKGROUND: Barriers to the early detection of mild cognitive impairment (MCI) and dementia can delay diagnosis and treatment. myMemCheck® was developed as a rapid free cognitive self-assessment tool that can be completed at home to identify older adults that would benefit from a more comprehensive cognitive evaluation. OBJECTIVE: Two prospective cross-sectional studies were conducted to examine the psychometric properties and clinical utility of myMemCheck®. METHODS: In Study 1, participants were independent living residents referred to an outpatient memory clinic (N = 59); older adults in the community and post-acute nursing home residents (N = 357) comprised Study 2. RESULTS: Psychometric analyses were performed on cognitive and psychological testing data, including myMemCheck®. myMemCheck® evidenced adequate reliability and strong construct validity. Receiver operating characteristic analysis evidenced an optional myMemCheck® cut score for identifying older adults at risk for MCI or dementia. myMemCheck® explained 25% of cognitive status beyond basic patient information. CONCLUSIONS: myMemCheck® may help fast-track the diagnostic process, facilitate appropriate referrals for cognitive and neuropsychological evaluation, reduce assessment burden in health care and prevent negative outcomes associated with undetected 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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".