Composite Indices of the Color-Picture Version of Boston Naming Test Have Better Discriminatory Power: Reliability and Validity in a Chinese Sample with Diverse Neurodegenerative Diseases
Bibliographic record
Abstract
BACKGROUND: The Boston Naming Test (BNT) is the most widely used measure to assess anomia. However, it has been criticized for failing to differentiate the underlying cognitive process of anomia. OBJECTIVE: We validated the color-picture version of BNT (CP-BNT) in a sample with diverse neurodegenerative dementia diseases (NDDs). We also verified the differential ability of the composite indices of CP-BNT across NDDs groups. METHODS: The present study included Alzheimer's disease (n = 132), semantic variant primary progressive aphasia (svPPA, n = 53), non-svPPA (n = 33), posterior cortical atrophy (PCA, n = 35), and normal controls (n = 110). We evaluated psychometric properties of CP-BNT for the spontaneous naming (SN), the percentage of correct responses on semantic cuing and word recognition cuing (% SC, % WR). Receiver operating characteristic analysis was used to examine the discriminatory power of SN alone and the composite indices (SN, % SC, and % WR). RESULTS: The CP-BNT had sufficient internal consistency, good convergent, divergent validity, and criterion validity. Different indices of CP-BNT demonstrated distinct cognitive underpinnings. Category fluency was the strongest predictor of SN (β= 0.46, p < 0.001). Auditory comprehension tests highly associated with % WR (Sentence comprehension: β= 0.22, p = 0.001; Word comprehension: β= 0.20, p = 0.001), whereas a lower visuospatial score predicted % SC (β= -0.2, p = 0.001). Composite indices had better predictability than the SN alone when differentiating between NDDs, especially for PCA versus non-svPPA (area under the curve increased from 63.9% to 81.2%). CONCLUSION: The CP-BNT is a highly linguistically relevant test with sufficient reliability and validity. Composite indices could provide more differential information beyond SN and should be used in clinical practice.
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.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".