Construct Validity of the Amyotrophic Lateral Sclerosis Bulbar Dysfunction Index–Remote
Bibliographic record
Abstract
PURPOSE: The Amyotrophic Lateral Sclerosis Bulbar Dysfunction Index-Remote (ALSBDI-R) is a clinician-administered tool designed to assess bulbar dysfunction remotely in patients with amyotrophic lateral sclerosis (ALS). This study aimed to evaluate the construct validity of the ALSBDI-R by examining its correlation with established clinical measures and its ability to discriminate among different bulbar disease severities. METHOD: A total of 92 patients with ALS were recruited from two multidisciplinary clinics. Participants were assessed using the ALSBDI-R, the Amyotrophic Lateral Sclerosis Functional Rating Scale-Revised (ALSFRS-R), the Center for Neurologic Study Bulbar Function Scale (CNS-BFS), the Sentence Intelligibility Test, and the Eating Assessment Tool (EAT-10). Construct validity was established through Spearman correlations and comparison of ALSBDI-R scores across bulbar severity groups (asymptomatic, mild, moderate, severe). RESULTS: = .77). The ALSBDI-R effectively discriminated between severity groups, supporting its construct validity. Severity bins were created based on median ALSBDI-R total scores for each group. CONCLUSIONS: The ALSBDI-R is a valid tool for remotely assessing bulbar dysfunction in patients with ALS. Despite several limitations, its ability to capture varying degrees of severity makes it valuable for clinical use and research, offering a standardized approach to monitor disease progression remotely.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".