Comparing Diagnostic Accuracy of Cognitive Screening Instruments: A Weighted Comparison Approach
Bibliographic record
Abstract
BACKGROUND/AIMS: There are many cognitive screening instruments available to clinicians when assessing patients' cognitive function, but the best way to compare the diagnostic utility of these tests is uncertain. One method is to undertake a weighted comparison which takes into account the difference in sensitivity and specificity of two tests, the relative clinical misclassification costs of true- and false-positive diagnosis, and also disease prevalence. METHODS: Data were examined from four pragmatic diagnostic accuracy studies from one clinic which compared the Mini-Mental State Examination (MMSE) with the Addenbrooke's Cognitive Examination-Revised (ACE-R), the Montreal Cognitive Assessment (MoCA), the Test Your Memory (TYM) test, and the Mini-Mental Parkinson (MMP), respectively. RESULTS: Weighted comparison calculations suggested a net benefit for ACE-R, MoCA, and MMP compared to MMSE, but a net loss for TYM test compared to MMSE. CONCLUSION: Routine incorporation of weighted comparison or other similar net benefit measures into diagnostic accuracy studies merits consideration to better inform clinicians of the relative value of cognitive screening instruments.
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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 | 0.380 | 0.560 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.011 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| 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".