Floor‐effect in the performance of the Spanish‐language MoCA by cognitively unimpaired Peruvian individuals with low educational background
Bibliographic record
Abstract
BACKGROUND: The Montreal Cognitive Assessment (MoCA) stands as a prominent cognitive impairment screening tool, finding widespread use globally and existing in official versions across 14 languages, including Spanish. Despite this, the challenges emerge due to the extensive variations within the Spanish language, which is not only the fourth most spoken language worldwide but also possesses significant geographic diversity, particularly evident in regions like Peru. Here, the intersection of regional nuances, low educational backgrounds, and culturally distinct tasks complicates the application of a standard MoCA version. We aimed to assess MoCA performance in individuals with both high and low education levels from an urban region of Peru to investigate the applicability of MoCA in low-educated population. METHODS: A total of 40 cognitively healthy individuals with low education (less than 6 years, mean = 3.8 ±1.8) and 40 individuals with higher education (more than 6 years, mean = 14.1 ±3.5) participated in the study. We assessed them with the Spanish version 7.0 of MoCA and estimated total score and domain indices. We fitted linear regression models to evaluate age- and sex-adjusted differences between groups. We examined the presence of a floor effect in the low education group, defining it as the test's minimum possible score surpassing the mean minus three standard deviations. This observation indicates a lack of sensitivity in capturing normal inter-individual variation at lower values. To delve deeper, we employed a three-parameter Item Response Theory model (IRT) to estimate the difficulty of each test item. RESULTS: We observed significant differences in the total MoCA score (b = 9.3, 95% CI: 7.7,11, p<0.001) as well as in the memory index (MoCA-MIS, b = 5.9, 95% CI: 4.4, 7.5, p<0.001), executive function (MoCA-EIS, b = 5.0, 95% CI:4.0,6.0 p<0.001), attention (MoCA-AIS, b = 5.5, 95% CI: 4.4, 6.6, p<0.001), and the remaining subscores (Figure 1). Floor effect was detected in memory, executive, language, and visuospatial indices. (Table 1) The more complex items identified were repetition and abstraction tasks (Figure 2) CONCLUSION: Our results shows that MoCA indices can be significantly influenced by education, floor effect is present even in healthy individuals, and certain items need to be reconsidered due to their difficulty in this context.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".