Comparative Diagnostic Accuracy Of ACE-III And MoCA For Detecting Mild Cognitive Impairment: A Letter To The Editor [Letter]
Bibliographic record
Abstract
Ellen Richards, Olivia Knowles Department of Medicine, Barts and the London School of Medicine and Dentistry, London, UKCorrespondence: Ellen RichardsDepartment of Medicine, Barts and the London School of Medicine and Dentistry, London, UKEmail richards@se13.qmul.ac.ukWe read with interest the article by Wang et al, looking into the reliability and diagnostic accuracy of Addenbrooke’s Cognitive Examination III (ACE-III), translated into Chinese, when looking at patients with mild cognitive impairment (MCI). The authors recruited 120 patients with MCI, and 136 healthy controls, and showed a positive correlation between ACE-III results and other common cognitive assessment methods (Montreal Cognitive Assessment (MoCA) and Mini-Mental State Examination (MMSE)). They also showed ACE-III to have higher diagnostic accuracy in detecting MCI when compared with the MMSE.1 We would like to thank the authors for highlighting the success of the Chinese version of ACE-III in diagnosing MCI and would like to offer some comments regarding their study.View the original paper by Wang and colleagues
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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 | 0.009 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.017 | 0.009 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".