Severe Impairment Battery Language scale: A language‐assessment tool for Alzheimer's disease patients
Bibliographic record
Abstract
BACKGROUND: Communication problems are common in Alzheimer's disease (AD) patients, but instruments to assess these symptoms are limited. Our objective was to create a new scale, based on the language subscale of the Severe Impairment Battery (SIB), as a sensitive and reliable measurement of treatment effects on language performance. METHODS: All 24 items of the SIB language subscale were chosen for analysis. Baseline scores of 1320 moderate-to-severe patients (Mini-Mental State Examination [MMSE] score, <15), from a combined AD database of four Memantine clinical trials (Study Codes: IE-2101, MEM-MD-01, MEM-MD-02, and MRZ-9605), were used for item reduction according to a standard principal components factor analysis. All items with loadings >0.5 on the identified factors were selected for inclusion in the new language scale. Correlations with existing AD scales were examined. RESULTS: The analysis indicated six factors, with 21 of 24 items showing loadings >0.5. The resulting 21-item SIB Language (SIB-L) scale exhibited high internal consistency (Cronbach's alpha = 0.809). The maximal SIB-L score was 41 points, with a measurement error of 3.7 points. The stratification of baseline SIB-L scores (mean, 31.7; SD, 8.4) by MMSE scores (mean, 9.7; SD, 3.3) showed a high variance in SIB-L scores. This confirms that patients with a low MMSE score can possess preserved language abilities. The SIB-L scale did not exhibit substantial floor-and-ceiling effects. CONCLUSIONS: The new SIB-L is a fast (<15 minutes) and easily administered scale with favorable psychometric characteristics for assessing language impairment and treatment effects on the language performance of patients with moderate to severe AD.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".