A systematic review of the diagnostic test accuracy of brief cognitive tests to detect amnestic mild cognitive impairment
Bibliographic record
Abstract
OBJECTIVE: People with amnestic mild cognitive impairment (aMCI) are at an increased risk of developing dementia. Efficient ways of identifying this 'at risk' population are required for larger-scale research studies. This systematic review describes the diagnostic accuracy of brief cognitive tests for detecting aMCI. METHODS: Fifteen databases were searched from 1999 to July 2013 to identify papers for inclusion. Prospective studies assessing the diagnostic test accuracy of simple and brief cognitive tests for identifying people with aMCI against a reference standard (Petersen criteria) were included. Sensitivity, specificity, positive and negative predictive values and likelihood ratios were calculated. Predictive validity and test-retest reliability were also extracted, when provided. Risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies tool. RESULTS: Thirty-nine studies assessing 42 index tests were included. The Montreal Cognitive Assessment was the most comprehensively assessed test with evidence of high sensitivity for aMCI and good test-retest reliability, but low specificity was reported by the only study judged to be at low risk of bias. Other brief cognitive tests that include an assessment of word recall and multi-task tests that assess several cognitive domains were also found to exhibit high sensitivities and reasonable specificities. However, the confidence of the findings was affected by overall low quality of the contributing studies. CONCLUSION: Several brief cognitive tests have shown promising diagnostic test accuracy results for identifying aMCI. However, concerns over the quality of the constituent studies and lack of evidence on the predictive validity of these tests mean that new validation studies are warranted. Copyright © 2016 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".