BNT–15: Revised Performance Validity Cutoffs and Proposed Clinical Classification Ranges
Bibliographic record
Abstract
BACKGROUND: Abbreviated neurocognitive tests offer a practical alternative to full-length versions but often lack clear interpretive guidelines, thereby limiting their clinical utility. OBJECTIVE: To replicate validity cutoffs for the Boston Naming Test-Short Form (BNT-15) and to introduce a clinical classification system for the BNT-15 as a measure of object-naming skills. METHOD: We collected data from 43 university students and 46 clinical patients. Classification accuracy was computed against psychometrically defined criterion groups. Clinical classification ranges were developed using a z -score transformation. RESULTS: Previously suggested validity cutoffs (≤11 and ≤12) produced comparable classification accuracy among the university students. However, a more conservative cutoff (≤10) was needed with the clinical patients to contain the false-positive rate (0.20-0.38 sensitivity at 0.92-0.96 specificity). As a measure of cognitive ability, a perfect BNT-15 score suggests above average performance; ≤11 suggests clinically significant deficits. Demographically adjusted prorated BNT-15 T-scores correlated strongly (0.86) with the newly developed z -scores. CONCLUSION: Given its brevity (<5 minutes), ease of administration and scoring, the BNT-15 can function as a useful and cost-effective screening measure for both object-naming/English proficiency and performance validity. The proposed clinical classification ranges provide useful guidelines for practitioners.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".