Cognitive screening instruments for dementia: comparing metrics of test limitation
Bibliographic record
Abstract
Cognitive screening instruments (CSIs) for dementia and mild cognitive impairment are usually characterized in terms of measures of discrimination such as sensitivity, specificity, and likelihood ratios, but these CSIs also have limitations. Objective: The aim of this study was to calculate various measures of test limitation for commonly used CSIs, namely, misclassification rate (MR), net harm/net benefit ratio (H/B), and the likelihood to be diagnosed or misdiagnosed (LDM). Methods: Data from several previously reported pragmatic test accuracy studies of CSIs (Mini-Mental State Examination, the Montreal Cognitive Assessment, Mini-Addenbrooke's Cognitive Examination, Six-item Cognitive Impairment Test, informant Ascertain Dementia 8, Test Your Memory test, and Free-Cog) undertaken in a single clinic were reanalyzed to calculate and compare MR, H/B, and the LDM for each test. Results: Some CSIs with very high sensitivity but low specificity for dementia fared poorly on measures of limitation, with high MRs, low H/B, and low LDM; some had likelihoods favoring misdiagnosis over diagnosis. Tests with a better balance of sensitivity and specificity fared better on measures of limitation. Conclusions: When deciding which CSI to administer, measures of test limitation as well as measures of test discrimination should be considered. Identification of CSIs with high MR, low H/B, and low LDM, may have implications for their use in clinical practice.
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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 | 0.051 | 0.170 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".