Bibliographic record
Abstract
Objective To assess Montreal cognitive assessment(MoCA)in different ways and MoCA's latent factorial structure with confirmatory factor analysis(CFA).Methods The MoCA and Mini Mental State Examination(MMSE)were taken to all subjects(thirty-two patients with AD,forty-two patients with MCI and twenty-nine normal controls)to compare the sensitivity of the MMSE and MoCA in screening the MCI、to assess the Internal consistency and correlation between the scores of MoCA and MMSE and to perform confirmatory factor analysis.Results The Sensitivity to distinguish MCI and AD were 81.0% and 100% respectively and MMSE were 20.0% and 93.8%.There was high correlation between the scores of MoCA and MMSE(r=0.911,P0.001).The measures of reliability concerning the MoCA showed good internal consistency(Cronbach's α=0.954).CFA results showed very good/excellent adjustment indexes.Conclusions In a clinical population,the MoCA is a valid and reliable instrument with good properties and good factorial structure.
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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 | 0.016 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".