Assessing functional impairment in individuals with mild cognitive impairment
Bibliographic record
Abstract
To date, there is no consensus on how to assess functional impairment in individuals with mild cognitive impairment (MCI), and this lack of consensus is reflected in the clinical practice. Since the criterion used in the literature is very vague, clinicians are still left without much guidance in this area. Thus, the main goal of this study was to examine how functional impairment in individuals with MCI has been assessed in the literature. An electronic database search strategy was developed in consultation with an experienced librarian. Four databases (CINAHL, PsycINFO, PubMed, and MEDLINE) were searched from 2000 to May 2014 to provide a comprehensive coverage of the literature. The literature search yielded 14 tools that assessed functional impairment in MCI. Among those, nine tools were performance-based measures in which participants were observed while executing a task in a simulated environment using real life material. In terms of questionnaires (either informant- or self-reports), five tools were found. Different functional domains have been assessed in each tool. According to this review, the characteristics of the instruments used in the literature to assess functional impairment in individuals with MCI vary greatly. Nonetheless, results of this study allow clinicians to make better-informed decisions when choosing a functional assessment for this population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.013 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".