Is the Quick Mild Cognitive Impairment Screen (QMCI) more accurate at detecting mild cognitive impairment than existing short cognitive screening tests? A systematic review of the current literature
Bibliographic record
Abstract
OBJECTIVES: Differentiating normal cognition, mild cognitive impairment (MCI), and dementia is important, as these conditions differ in terms of their prognosis and treatment. Existing short cognitive screening tests vary widely in their accuracy, sensitivity, and specificity at detecting MCI and dementia. The Quick Mild Cognitive Impairment Screen (QMCI) was developed in 2012 as a fast and accurate "MCI specific" screening test. The aim of the current study was to conduct a literature review to compare the accuracy, sensitivity, and specificity of the QMCI at differentiating normal cognition, MCI, and dementia to existing short cognitive screening tests at their optimal cut-off scores. METHODS: A search of the electronic journal databases EBSCO, Psych info, and Science Direct was undertaken using the keywords "Quick Mild Cognitive Impairment Screen," "QMCI," "accuracy," "sensitivity," and "specificity." Results of individual studies were examined, and 2 × 2 tables were drawn up to obtain the overall accuracy, sensitivity, and specificity of each test across the studies included. RESULTS: Results from individual studies show that the QMCI has higher accuracy at detecting MCI and dementia than these cognitive screens. Pooled analysis shows that it also has greater sensitivity and specificity at optimal cut-off points for each test. CONCLUSIONS: Based in the current review, the QMCI represents a more accurate, sensitive, and specific screening test for MCI and dementia than the SMMSE or the MoCA. This has important implications in screening for cognitive impairment.
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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 | 0.022 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.016 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".