P2‐355: HOW HAS FUNCTIONAL IMPAIRMENT BEEN ASSESSED IN INDIVIDUALS WITH MCI? A REVIEW OF THE LITERATURE
Bibliographic record
Abstract
Assessment of functional performance is a key component on the early diagnosis of mild cognitive impairment (MCI). However, to date there is no consensus on how to assess functional performance in this population. It is currently very challenging to draw conclusions from the MCI literature in this area because different research groups are using instruments with different properties, lack of psychometric rigor in most of the instruments used and some researchers still rely on questionnaires that were developed to be used with dementia patients. As a consequence, clinicians are still left without much guidance on how to assess functional performance in this population. The goal of our study was to examine how functional performance has been specifically defined and assessed in the literature. We conducted a scoping review of the literature by searching MEDLINE, CINAHL and, PsychINFO, from 2000 to 2013 to look for functional assessments that have been used to detect MCI. The following search terms, both as MeSH terms (in italics) and as keywords, were used to identify potentially relevant studies. The terms were first searched in Medline and then subsequently tailored for searches in other databases: mild cognitive impairment (MeSH) or cognition disorder (MeSH) and functional assessments (MeSH) or and activities of daily living (MeSH) or activit*. Studies were eligible if they were published in English and used an instrument to assess functional performance in individuals with MCI. The instruments were then classified into type (e.g., performance-based, questionnaire, informant report, etc...), performance based components assessed (organization, initiation, planning, remembering, etc...), MCI subtype and scoring. Several studies examining functional assessments in individuals with MCI have been identified. The studies differed in terms of MCI classification, types of assessments, scoring and performance based components of the assessments. The results of this study will help us to have a better understanding of how functional criteria have been utilized in the literature. It will further help us to map out the specific gaps in this area and propose approaches to close those gaps.
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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 | 0.009 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.021 | 0.022 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".