Protocol for psychometric evaluation of the Amyotrophic Lateral Sclerosis - Bulbar Dysfunction Index (ALS-BDI): a prospective longitudinal study
Bibliographic record
Abstract
INTRODUCTION: Early detection and tracking of bulbar dysfunction in amyotrophic lateral sclerosis (ALS) are critical for directing management of the disease. Current clinical bulbar assessment tools are lacking, while existing physiological instrumental assessments are often inaccessible and cost-prohibitive for clinical application. The goal of our research is to develop and validate a brief and reliable, clinician-administered assessment tool-the ALS-Bulbar Dysfunction Index (ALS-BDI). This publication describes the study protocol that has been established to ascertain the tools' psychometric properties. METHODS AND ANALYSIS: The ALD-BDI's development closely follows guidelines outlined by the COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN). Through the proposed study protocol, we expect to establish psychometric properties of both individual test items of the ALS-BDI as well as the final version of the entire tool, including test-retest and inter-rater reliability, construct validity using gold-standard assessment methods and responsiveness. ETHICS AND DISSEMINATION: This study has been reviewed and approved by research ethics boards at two data collection sites: Sunnybrook Health Science Centre, primary (Toronto, Canada; ID3080) and Mass General Brigham (#2013P001746, Boston, USA). Prior to participation in the study, the participants sign the informed consent in accordance with the Declaration of Helsinki. Once validated, the ALS-BDI will be disseminated to key stakeholders. Following validation, the ALS-BDI and any required training material will be implemented for clinical use in a context of a multidisciplinary ALS clinic and used as an outcome measure for clinical trials in ALS research.
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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 | 0.072 | 0.091 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.066 | 0.027 |
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".