Comparative analysis of five diagnostic tools in detecting mild cognitive impairment in older adults
Bibliographic record
Abstract
Abstract Background Mild Cognitive Impairment is a critical condition in older adults requiring accurate diagnostic tools for early detection. This study evaluates diagnostic accuracy of five cognitive assessment tools for detecting MCI among older women. Methods A cross-sectional psychometric study was conducted with 293 women aged ≥ 60 from Women Day Care Centers in Iran. Participants were assessed using the Montreal Cognitive Assessment at two time points, the London Tower Test, the Wisconsin Card Sorting Test, and the Wechsler Memory Scale-Third Edition. Statistical analyses included binomial proportion tests, Bayesian analysis, chi-square tests, and Bland–Altman analysis to assess diagnostic performance, agreement, and reliability. Sensitivity, specificity, and accuracy were calculated using R and JAMOVI softwares. Results The WCST demonstrated the highest specificity (0.850) and strong evidence for detecting cognitive impairments (BF₁₀ = 5.24E + 13, p < 0.001). The WMS-III showed the highest sensitivity (0.700) and accuracy (0.625). MoCA scores improved slightly from T1 (mean = 23.03) to T2 (mean = 24.56), but its reliability varied. The LTT provided moderate evidence for detecting impairments ( p = 0.026, BF₁₀ = 0.9778). Socioeconomic status and education significantly influenced cognitive performance, with 46.8% diagnosed with MCI. Agreement between human diagnosis and tool-based assessments was significant ( p < 0.001), particularly for WCST and WMS-III. Conclusion The WCST and WMS-III are the most reliable tools for detecting MCI, excelling in specificity and sensitivity, respectively. Combining multiple tests enhances diagnostic accuracy. Future research should explore larger populations and integrate advanced methods like neuroimaging.
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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 | 0.014 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".