Improving Scoring Precision and Internal Construct Validity of the Bath Ankylosing Spondylitis Disease Activity Index Using Rasch Measurement Theory
Bibliographic record
Abstract
OBJECTIVE: To test the Bath Ankylosing Spondylitis Disease Activity Index (BASDAI) according to Rasch Measurement Theory and investigate whether measurement precision can be improved. METHODS: Secondary analysis of a BASDAI database. The data had been collected from individuals starting an Ankylosing Spondylitis Exercise Course at the Royal National Hospital for Rheumatic Diseases in Bath, UK. RESULTS: Data were available for 250 participants (23.6% female) aged between 18 and 85 years (mean 52.8, SD 14.6). Initial fit of the data to the Rasch model appeared good and item thresholds were consistent, but local item dependence (LID) was identified. After addressing the LID, a unidimensional measure was achieved. The Person Separation Index (reliability) was 0.83 and the location of the items was well matched to that of the respondents. A transformation table was generated to convert total raw BASDAI scores into linearized Rasch transformed scores that form an interval scale. The Smallest Detectable Difference improved from 2 to 1.2. This finding suggests that a change score of > 1.2 points on the modified BASDAI is required to achieve meaningful change. CONCLUSION: Applying the Rasch transformed scores simplifies completion and scoring of the measure and confirms internal construct validity. It also ensures linear measurement and justifies the use of parametric statistical analyses when analyzing datasets. The transformation table can be used with existing BASDAI datasets to allow direct comparisons of disease activity scores with those generated from future studies.
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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 | 0.123 | 0.223 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".