<p>Comparative diagnostic accuracy of ACE-III and MoCA for detecting mild cognitive impairment</p>
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to validate the reliability of the Chinese version of Addenbrooke's Cognitive Examination III (ACE-III) for detecting mild cognitive impairment. Furthermore, the present study compares the diagnostic accuracy of ACE-III with that of Montreal Cognitive Assessment (MoCA). METHODS: One hundred and twenty patients with MCI and 136 healthy controls were included in the study. All patients were evaluated by the Chinese version of ACE-III, MoCA and MMSE. RESULTS: Subjects in the control group showed better performance in ACE-III total score and its subdomain scores than those in the MCI group. There was a significantly positive correlation between ACE-III total score and MoCA score. Meanwhile, there was also a significantly positive correlation between ACE-III total score and MMSE score. For ACE-III total score, a cut-off point of 85 yielded a sensitivity of 97.3% and a specificity of 90.7%. The AUC for ACE-III total score was 0.978. For MoCA, a cut-off point of 23 yielded a sensitivity of 86.5% and a specificity of 97.7%. The AUC for MoCA was 0.961. There were no significant differences in diagnostic accuracy between ACE-III and MoCA. CONCLUSION: The present findings support that both ACE-III and MoCA are useful for detecting MCI in early stages.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it