What is Test Accuracy? Comparing Unitary Accuracy Metrics for Cognitive Screening Instruments
Bibliographic record
Abstract
Aim: To examine four different accuracy metrics for assessment of commonly used cognitive screening instruments: correct classification accuracy, area under the receiver operating characteristic curve, F measure (F) or F1 score and Matthews correlation coefficient (MCC). Methods: Raw data were extracted from test accuracy studies of Mini-Mental State Examination. Montreal Cognitive Assessment, Mini-Addenbrooke's Cognitive Examination, Six-item Cognitive Impairment Test, informant AD8 and Free-Cog, and used to calculate the accuracy measures. Results: Each metric resulted in similar ordering of the screening instruments for diagnosis of both dementia and mild cognitive impairment. Area under the receiver operating characteristic curve gave the highest (most optimistic) and MCC the lowest (most pessimistic) accuracy value for each test examined, with correct classification accuracy and F falling between. Conclusion: All the accuracy measures examined have potential shortcomings. None can be recommended as the definitive unitary outcome measure for test accuracy studies. However, MCC has theoretical advantages and might be more widely adopted.
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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 | 0.147 | 0.506 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".