Effect Size (Cohen's d) of Cognitive Screening Instruments Examined in Pragmatic Diagnostic Accuracy Studies
Bibliographic record
Abstract
BACKGROUND/AIMS: Many cognitive screening instruments (CSI) are available to clinicians to assess cognitive function. The optimal method comparing the diagnostic utility of such tests is uncertain. The effect size (Cohen's d), calculated as the difference of the means of two groups divided by the weighted pooled standard deviations of these groups, may permit such comparisons. METHODS: Datasets from five pragmatic diagnostic accuracy studies, which examined the Mini-Mental State Examination (MMSE), the Mini-Mental Parkinson (MMP), the Six-Item Cognitive Impairment Test (6CIT), the Montreal Cognitive Assessment (MoCA), the Test Your Memory test (TYM), and the Addenbrooke's Cognitive Examination-Revised (ACE-R), were analysed to calculate the effect size (Cohen's d) for the diagnosis of dementia versus no dementia and for the diagnosis of mild cognitive impairment versus no dementia (subjective memory impairment). RESULTS: The effect sizes for dementia versus no dementia diagnosis were large for all six CSI examined (range 1.59-1.87). For the diagnosis of mild cognitive impairment versus no dementia, the effect sizes ranged from medium to large (range 0.48-1.45), with MoCA having the largest effect size. CONCLUSION: The calculation of the effect size (Cohen's d) in diagnostic accuracy studies is straightforward. The routine incorporation of effect size calculations into diagnostic accuracy studies merits consideration in order to facilitate the comparison of the relative value of CSI.
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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 | 0.450 | 0.714 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.023 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".