The Frontal Assessment Battery (FAB) and its sub-scales: validation and updated normative data in an Italian population sample
Bibliographic record
Abstract
BACKGROUND: Deficits of executive functioning (EF) are frequently found in neurological disorders. The Frontal Assessment Battery (FAB) is one of the most widespread and psychometrically robust EF screeners in clinical settings. However, in Italy, FAB norms date back to 15 years ago; moreover, its validity against "EF-loaded" global cognitive screeners (e.g., the Montreal Cognitive Assessment, MoCA) has yet to be tested. This study thus aimed at (a) providing updated normative data for the Italian FAB and (b) assessing its convergent validity with the MoCA. METHODS: Four-hundred and seventy-five healthy Italian native speakers (306 females, 169 males; mean age: 61.08 ± 15.1; mean education: 11.67 ± 4.57) were administered by the MoCA and the FAB. FAB items were divided into three subscales: FAB-1 (linguistically mediated EF), FAB-2 (planning), and FAB-3 (inhibition). Regression-based norms were derived (equivalent scores) for all FAB measures. RESULTS: Age and education were predictive of all FAB measures, whereas no gender differences were detected. The FAB and its sub-scales were related to MoCA measures-the strongest associations being found with MoCA total and MoCA-EF scores. FAB sub-scales were both internally related and associated with FAB total scores. DISCUSSION: The FAB proved to have convergent validity with both global cognitive and EF measures in healthy individuals. The present study provides updated normative data for the FAB and its sub-scales in an Italian population sample, and thus supports an adaptive usage of this EF screener.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".