Does height influence the assessment of spinal and hip mobility measures used in ankylosing spondylitis?
Bibliographic record
Abstract
OBJECTIVE: It is not known if height contributes to the variability in mobility measures in patients with ankylosing spondylitis (AS) and whether any measures should be reported corrected for height. We examined the contribution of height to the variability of mobility measures in patients with a diverse spectrum of AS disease. METHODS: We assessed the 9 mobility measures comprising the Bath AS and Edmonton AS Metrology Indices (BASMI and EDASMI) in a total of 205 patients. The contribution of height to the variability in mobility scores was analyzed descriptively according to tertiles of height, and also by combined probability scatter plots that combined each individual's height with the corresponding score for either the composite index or each of the 9 spinal mobility measures. Hierarchical (sequential) linear regression was used to assess the contribution of height to the variance in EDASMI and BASMI composite scores and individual measures, adjusted for age, disease duration, and the Bath AS Disease Activity Index. RESULTS: Descriptive data and correlation analysis revealed significant differences related to height for both the EDASMI and the BASMI, particularly for EDASMI cervical rotation, EDASMI lumbar side flexion, chest expansion, lumbar flexion, and intermalleolar distance. Combined probability scatter plots showed that for a particular height there was a wide distribution of mobility scores and only intermalleolar distance showed some relation to height. Hierarchical regression analysis showed that height contributed significantly, although relatively minimally to the variance of both the EDASMI (3.1%; p <or= 0.05) and the BASMI (3.6%; p <or= 0.05), but only to EDASMI cervical rotation among individual mobility measures (variance of 7.0%; p
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".