Evaluación de funcionamiento cognitivo en adultos: Análisis y contrastación de tres de los instrumentos de mayor divulgación en Chile
Bibliographic record
Abstract
Background Several instruments are available to measure cognitive functioning in older adults. However, there is paucity of information about their factorial structure and psychometric properties. Aim To determine the factorial structure and the internal reliability of the Mini Mental State Examination (MMSE), the Montreal Cognitive Assessment (MoCA) and the Adenbrookke´s Cognitive Examination (ACE-R), and their cognitive impairment detection capabilities. Material and Methods MMSE, MoCA and ACE-R were applied to 203 older adults aged 54 to 88 years (77% women), excluding participants with dementia. Results The factorial structure of the MMSE suggested that items referred to memory process should be eliminated due to their low reliability and factor loading (b = 0.12; p = 0.146). Although the MoCA had a good reliability, object denomination process items also had to be dropped (b = 0.22; p = 0.003). The ACE-R demonstrated a single factorial structure for all cognitive processes and had a good internal consistency. MMSE, MoCA and ACE-R classified as having dementia 5, 27 and 42% of participants, respectively. Conclusions MoCA and the ACE-R scales appear as better instruments to detect dementia in older people.
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 | 0.017 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".