Concordancia entre las pruebas Mini Mental State Examination, Short Portable Mental Status Questionnarie y Montreal Cognitive Assesment para el tamizaje del deterioro cognitivo en adultos mayores
Bibliographic record
Abstract
Objective: The objective of the study is to determine the level of concordance between the MMSE, SPMSQ and MoCA tests for the screening of cognitive impairment in older adults through the Kappa index between the three tests. \n \nMaterial and methods: Retrospective cohort study in people attended in the Geriatrics service of the Naval Medical Center "Cirujano Mayor Santiago Távara", selected by convenience. A total of 1683 patient were included, taking as points of cut to determine cognitive deterioration a score higher than 4 in SPMSQ; a score lower than 26 in MoCA; and a score lower than 25 in MMSE. Cohen's Kappa Index was used, using a value of 0.8 as an indicator of good concordance between them. \n \nResults: The MMSE was the test that found the largest number of patients with cognitive impairment, giving 43.32% of the total. A good level of concordance between the MMSE and MoCA tests was observed (Kappa index: 0.99 IC95% 0.99 - 1.00 p <0.01), and a discordant result between the MoCA and SPMSQ tests (Kappa index: 0.42 95% CI 0.38 - 0.46 p <0.01); and the MMSE and SPMSQ tests (Kappa index: 0.42 IC95% 0.38 - 0.46 p <0.01) \n \nConclusion: The MoCA and MMSE tests maintain an excellent concordance between them. While, the result of the SPMSQ test is discordant with respect to the others (MMSE / MoCA).
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".