Web-based cognitive assessment in older adults: Where do we stand?
Bibliographic record
Abstract
PURPOSE OF REVIEW: The use of digital tools for remote cognitive measurement of older adults is generating increasing interest due to the numerous advantages offered for accessibility and scalability. However, these tools also pose distinctive challenges, necessitating a thorough analysis of their psychometric properties, feasibility and acceptability. RECENT FINDINGS: In this narrative review, we present the recent literature on the use of web-based cognitive assessment to characterize cognition in older adults and to contribute to the diagnosis of age-related neurodegenerative diseases. We present and discuss three types of web-based cognitive assessments: conventional cognitive tests administered through videoconferencing; unsupervised web-based assessments conducted on a computer; and unsupervised web-based assessments performed on smartphones. SUMMARY: There have been considerable progress documenting the properties, strengths and limitations of web-based cognitive assessments. For the three types of assessments reported here, the findings support their promising potential for older adults. However, certain aspects, such as the construct validity of these tools and the development of robust norms, remain less well documented. Nonetheless, the beneficial potential of these tools, and their current validation and feasibility data, justify their application [see Supplementary Digital Content (SDC), http://links.lww.com/CONR/A69 ].
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".