Adaptation and norm determination of the Boston Naming Test for healthy Lebanese adults aged between 50 and 88 years
Bibliographic record
Abstract
Abstract The Boston Naming Test is a well-known neuropsychological test widely used to evaluate linguistic abilities, encompassing object naming and word retrieval in subjects representing various clinical pathologies. Our study has two main stages: (1) a pilot study aimed at adapting the BNT to the linguistic and cultural particularities of Lebanese society and (2) norm determination for the Lebanese version of the BNT through the analysis of participants’ responses. The primary goal of this study is to develop a Lebanese version of the BNT comprising 60 images adapted to the Lebanese language and culture. This version is based on normative data derived from healthy Lebanese adults aged between 50 and 88 years. The study seeks to assess the influence of age, gender, and education level on the naming performance of participants. In the pilot study, 103 Lebanese volunteers participated, while the normative study involved 280 healthy volunteers aged between 50 and 88 years. Three screening tests—Montreal Cognitive Assessment (MOCA), Language Experience and Proficiency Questionnaire (LEAP-Q), and Geriatric Depression Scale 15-item (GDS)—were administered to select participants meeting inclusion criteria. The findings revealed a statistically significant effect of age and education level on the BNT (Lebanese version) total score. The total score decreased with age and increased with education. However, the effect of gender was not significant, a result confirmed by the generalized linear model. This study successfully produced a Lebanese version of the BNT comparable to the original English version. Additionally, it provided normative data crucial for evaluating naming ability, word retrieval, and detecting potential disorders associated with aging.
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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 | 0.002 | 0.006 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".