Item-Level Scores on the Boston Naming Test as an Independent Predictor of Perirhinal Volume in Individuals with Mild Cognitive Impairment
Bibliographic record
Abstract
We explored the methodological value of an item-level scoring procedure applied to the Boston Naming Test (BNT), and the extent to which this scoring approach predicts grey matter (GM) variability in regions that sustain semantic memory. Twenty-seven BNT items administered as part of the Alzheimer’s Disease Neuroimaging Initiative were scored according to their “sensorimotor interaction” (SMI) value. Quantitative scores (i.e., the count of correctly named items) and qualitative scores (i.e., the average of SMI scores for correctly named items) were used as independent predictors of neuroanatomical GM maps in two sub-cohorts of 197 healthy adults and 350 mild cognitive impairment (MCI) participants. Quantitative scores predicted clusters of temporal and mediotemporal GM in both sub-cohorts. After accounting for quantitative scores, the qualitative scores predicted mediotemporal GM clusters in the MCI sub-cohort; clusters extended to the anterior parahippocampal gyrus and encompassed the perirhinal cortex. This was confirmed by a significant yet modest association between qualitative scores and region-of-interest-informed perirhinal volumes extracted post hoc. Item-level scoring of BNT performance provides complementary information to standard quantitative scores. The concurrent use of quantitative and qualitative scores may help profile lexical–semantic access more precisely, and might help detect changes in semantic memory that are typical of early-stage Alzheimer’s disease.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".