Validity and Reliability of the New Chinese Version of the Frontal Assessment Battery-Phonemic
Bibliographic record
Abstract
BACKGROUND: Alzheimer's disease dementia (ADD) is an important health problem in the world. OBJECTIVE: The present study investigated the validity and reliability of a new version of the Frontal Assessment Battery (FAB) named the FAB-phonemic (FAB-P). METHODS: A total of 76 patients with ADD, 107 patients with amnestic mild cognitive impairment (aMCI), 37 patients with non-amnestic MCI (naMCI), and 123 healthy controls were included in this study. All participants were evaluated with the FAB-P and the cognitive assessments according to a standard procedure. RESULTS: The global FAB-P scores in patients with ADD were lower than those of patients with aMCI, patients with naMCI, and healthy controls (p < 0.001). Patients with aMCI performed worse than healthy controls (p < 0.001). The interrater reliability, test-retest reliability, and Cronbach's alpha coefficient for the FAB-P were 0.997, 0.819, and 0.736, respectively. The test could distinguish the patients with mild ADD, aMCI, and naMCI from healthy controls with classification accuracy of 89.4%, 70.9%, and 61.6%, respectively. It could also discriminate between the patients with ADD and aMCI, between those with ADD and naMCI, and between those with aMCI and naMCI with classification accuracy of 73.8%, 83.9%, and 58.0%, respectively. The regression analysis revealed that the Montreal Cognitive Assessment and the Stroop Color Word Test Part C had the greatest contribution to FAB-P score variance. CONCLUSION: The FAB-P is a valid and reliable tool for evaluating frontal lobe function and can effectively discriminate ADD, aMCI, and naMCI.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".