Performance-based Tools for Assessing Functional Performance in Individuals with Mild Cognitive Impairment
Bibliographic record
Abstract
Background: It is now recognized that individuals with mild cognitive impairment (MCI) face subtle functional declines that can compromise performance in everyday tasks. However, it is still not clear how to capture these declines in the clinical setting. Thus, the goal of this study was to conduct a scoping review to identify performance-based tools for which the psychometric properties have been evaluated with the MCI population. Methods: A scoping review of the scientific literature was performed with the guidance of a health science librarian in searching the MEDLINE, PsychINFO, CINAHL, and EMBASE databases from their inception until May 2014. Results: Nine performance-based tools assessing functional performance in individuals with MCI have been identified in the literature. While construct and content validity have been extensively reported, only two tools provided data on reliability. Conclusion: Considering that functional decline is part of the normal aging process, it might be challenging to differentiate normal from pathological functional decline in this population. Functional measurement tools might be very sensitive to capture these subtle changes. Although no recommendations can be proposed at this point on a specific tool to assess functional performance in MCI, research in this area is beginning to identify the elements that should be taken into consideration when choosing a tool.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".