Digital Migration of the Loewenstein Acevedo Scales for Semantic Interference and Learning (LASSI-L): Development and Validation Study in Older Participants
Bibliographic record
Abstract
BACKGROUND: The early detection of mild cognitive impairment (MCI) is crucial for providing treatment before further decline. Cognitive challenge tests such as the Loewenstein-Acevedo Scales for Semantic Interference and Learning (LASSI-L™) can identify individuals at highest risk for cognitive deterioration. Performance on elements of the LASSI-L, particularly proactive interference, correlate with the presence of critical Alzheimer's Disease (AD) biomarkers. However, in person paper tests require skilled testers and are not practical in many community settings or for large-scale screening in prevention. OBJECTIVE: This paper reports on the development and initial validation of a self-administered computerized version of the LASSI, the LASSI-D™. A fully remotely deliverable digital version, with an AI generated avatar assistant, was the migrated assessment. METHODS: Cloud-based software was developed, using voice recognition technology, for English and Spanish versions of the LASSI-D. Participants were assessed with either the LASSI-L or LASSI-D first, in a sequential assessment study. Participants with amnestic Mild Cognitive Impairment (aMCI; n=54) or normal cognition (NC;n=58) were also tested with traditional measures such as the ADAS-Cog. We examined group differences in performance across the legacy and digital versions of the LASSI, as well as correlations between LASSI performance and other measures across the versions. RESULTS: Differences on recall and intrusion variables between aMCI and NC samples on both versions were all statistically significant (all p<.001), with at least medium effect sizes (d>.68). There were no statistically significant performance differences in these variables between legacy and digital administration in either sample, (all p<.13). There were no language differences in any variables, p>.10, and correlations between LASSI variables and other cognitive variables were statistically significant (all p<.01). The most predictive legacy variables, Proactive Interference (PI) and Failure to recover from Proactive Interference (frPI), were identical across legacy and migrated versions within groups and were identical to results of previous studies with the legacy LASSI-L. Classification accuracy was 88% for NC and 78% for aMCI participants. CONCLUSIONS: The results for the digital migration of the LASSI-D were highly convergent with the legacy LASSI-L. Across all indices of similarity, including sensitivity, criterion validity, classification accuracy, and performance, the versions converged across languages. Future papers will present additional validation data, including correlations with blood-based AD biomarkers and alternative forms. The current data provide convincing evidence of the utility of a fully self-administered digitally migrated cognitive challenge test.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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 | 0.001 |
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".