Optimising the Cutoffs of Cognitive Screening Instruments in Pragmatic Diagnostic Accuracy Studies: Maximising Accuracy or the Youden Index?
Bibliographic record
Abstract
BACKGROUND/AIMS: The optimal method of establishing test cutoffs or cutpoints for cognitive screening instruments (CSIs) is uncertain. Of the available methods, two base cutoffs on either the maximal test accuracy or the maximal Youden index. The aim of this study was to compare the effects of using these alternative methods of establishing cutoffs. METHODS: Datasets from three pragmatic diagnostic accuracy studies which examined the Mini-Mental State Examination (MMSE), the Addenbrooke's Cognitive Examination-Revised (ACE-R), the Montreal Cognitive Assessment (MoCA), and the Test Your Memory (TYM) test were analysed to calculate test sensitivity and specificity using cutoffs based on either maximal test accuracy or the maximal Youden index. RESULTS: For ACE-R, MoCA, and TYM, optimal cutoffs for dementia diagnosis differed from those in index studies when defined using either the maximal accuracy or the maximal Youden index method. Optimal cutoffs were higher for MMSE, MoCA, and TYM when using the maximal Youden index method and consequently more sensitive. CONCLUSION: Revision of the cutoffs for CSIs established in index studies may be required to optimise performance in pragmatic diagnostic test accuracy studies which more closely resemble 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 | 0.407 | 0.685 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| 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".