Detecting mild cognitive impairment remotely with the modified memory impairment screen by telephone
Bibliographic record
Abstract
The original Memory Impairment Screen by Telephone (MIST) was designed to identify individuals with dementia but was relatively ineffective for identification of less severe impairment observed in mild cognitive impairment (MCI). We expanded the original MIST to create a modified instrument (mMIST) with greater sensitivity to less severe memory impairment. Older men and women with subjective cognitive decline were assessed by phone with the mMIST and subsequently classified independently with MCI or non-pathological cognitive decline. Participants with MCI produced lower scores on the mMIST than did participants without MCI, 10.8 ± 2.7 vs 13.3 ± 1.3, t = 5.68, p < 0.001, and performance on the mMIST predicted performances on the California Verbal Learning Test (CVLT), Verbal Paired Associate Learning Test (VPAL), Montreal Cognitive Assessment (MoCA) total score, and MoCA memory index score, p < 0.001. Receiver operating characteristic (ROC) analyses identified the optimal cut score on the mMIST to distinguish participants with and without MCI with Sensitivity = 73.1%, Specificity = 79.1%, and AUC = 0.79. Predictive values for distinguishing the amnestic form of MCI (aMCI) from non-amnestic MCI were Sensitivity = 81.8%, Specificity = 30%, and AUC = 0.82. These findings indicate that the mMIST is a valid screening instrument for identifying MCI. It can be administered remotely at low cost and low participant burden. Also, the mMIST has potential utility for remote cross-sectional and longitudinal evaluation in research and clinical contexts. Further investigation is indicated to corroborate its utility for assessment of aging patients and research participants.
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How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".