Diagnostic Accuracy of Cognitive Screening Tools Validated for Older Adults in Iran: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Abstract Background This systematic review aims to comprehensively assess the diagnostic accuracy of cognitive screening tools validated for older adults in Iran, providing evidence-based recommendations for clinicians and researchers. Methods Multiple databases were searched for cross-sectional research published until March 2033. Inclusion criteria encompassed paper and pencil cognitive screening tools used in Iranian seniors. Data extraction involved evaluating diagnostic accuracy measures, cognitive domains, and strengths/weaknesses of each test. A bivariate random-effects meta-analysis generated summary estimates with 95% CIs, and forest plots visually represented the findings. Results The review included 17 studies investigating 14 cognitive screening instruments. Diagnostic accuracy data were extracted for the Clock Drawing Test (CDT), Mini-Cog, short portable mental status questionnaire (SPMSQ), A Quick Test of Cognitive Speed (AQT), Quick Mild Cognitive Impairment (Qmci) screen, Rowland Universal Dementia Assessment (RUDAS), Picture-Based Memory Impairment Screen (PMIS), Abbreviated Mental Test Score (AMTS), Mini–Mental State Examination (MMSE), Modified Mini-Mental State Examination (3MS), Montreal Cognitive Assessment (MoCA), Addenbrooke’s Cognitive Examination (ACE)-III, Persian test of Elderly for Assessment of Cognition and Executive function (PEACE), and Rey Auditory Verbal Learning Test (RAVLT). Pooled values from the bivariate effect model for the MMSE showed a sensitivity of 0.97, specificity of 0.87, DOR of 242, LR + of 7.69, and LR- of 0.03. Conclusion The results showed that the ACE-III demonstrated the highest accuracy for dementia and mild cognitive impairment (MCI) in specialized care settings. However, the high risk of bias in many studies emphasizes the need for more rigorous validations in diverse clinical contexts and populations.
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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 | 0.019 | 0.059 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.024 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".