Puntuaciones del MoCA y el MMSE en pacientes con deterioro cognitivo leve y demencia en una clínica de memoria en Bogotá
Bibliographic record
Abstract
Introduction. Some cognitive tests allow the evaluation of cognitive functions on the elderly in a short period of time. There are few studies in Colombia about cut-off point for the MMSE and the MoCA test. Objectives. To describe the distribution on scores on MMSE and MoCA test and the cut-off point with a better discrimination criteria for the diagnosis of mild cognitive impairment and dementia, in a sample of patients from Bogota. Materials and methods. Two hundred forty eight patients were included in this study, being evaluated by a multidisciplinary team that followed an established protocol, on patients who attended to the Memory Clinic of HIUSJ between 2009-2012. MoCA test and MMSE scores that allow higher percentages of correctly classified patients were identified. Results. Seventy percent of patients with mild cognitive impairment and 69% of normal individuals had scores on MMSE below or equal to 28. Ninety-one percent of patients with MCI and 89% of normal patients, had scores below or equal to 25. Patients with any type of dementia had scores on MMSE below or equal to 27 and below or equal to 24 in MoCA test. Conclusion. According to the study, the screening of cognitive functions, using MoCA test, is more accurate than MMSE in patients with cognitive decline. The cut-off points, identified in our study, can be considered useful until now in primary attention, in patients with a high level of education.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".