Frontal Assessment Battery to Evaluate Frontal Lobe Dysfunction in ALS Patients
Bibliographic record
Abstract
BACKGROUND: Assessment of frontal lobe impairment in amyotrophic lateral sclerosis (ALS) is a matter of great importance, since it often causes ALS patients to decrease medication and nursing compliance, thus shortening their survival time. METHODS: The frontal assessment battery (FAB) is a short and rapid method for assessing frontal executive functions. We investigated the applicability of the FAB as a screening method for assessing cognitive impairments in 61 ALS patients. Depending on the results of the FAB, we classified patients into two subgroups: FAB-normal and FAB-abnormal. We then performed additional evaluations of cognitive function using the Korean version of the mini-mental state examination (K-MMSE), a verbal fluency test (COWAT), and a neuropsychiatric inventory (NPI). Results of these tests were compared between the two groups using Mann-Whitney U-tests, and Spearman correlation analyses were used to investigate the relationships between FAB score and disease duration and severity. RESULTS: Of the 61 sporadic ALS patients included in this study, 14 were classified as FAB-abnormal and 47 were classified as FAB-normal. The FAB-normal and FAB-abnormal patients performed significantly differently in all domains of the COWAT. There was no difference in behavioral disturbance, as assessed by the NPI, between the two groups. The FAB scores were found to significantly correlate with both disease duration and severity. CONCLUSIONS: The FAB shows promise as a method of screening for frontal lobe dysfunction in ALS, as it is not only quick and easy, but also reliable. Additional studies should examine how FAB performance changes as ALS progresses.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.002 | 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".