The relationship between changes in self-reported disability (measured by the Health Assessment Questionnaire - HAQ) in scleroderma and improvement of disease status in clinical practice.
Bibliographic record
Abstract
OBJECTIVES: To determine if a low Health Assessment Questionnaire Disability Index (HAQ-DI) score predicts subsequent improvement over the next one to two years in clinical practice and if a low HAQ is predictive of improvement in early, late, diffuse and limited SSc subsets. METHODS: HAQs collected at one site annually were used to determine serial relationships in low baseline HAQ and improvement in overall status over the following one to two years. Data were divided into early (< or =3 years) and late, and then further into limited and diffuse SSc subgroups. We verified our results in the Canadian Scleroderma Research Group (CSRG) database. RESULTS: 120 SSc patients had a baseline HAQ-DI of 0.97+/-0.07 (SEM). Low HAQs predicted improvement in overall HAQ at one and two years, but was not statistically significant in predicting physician improvement rating. However, improving HAQs were associated with improvement in physician assessment (better vs. same vs. worse) for overall SSc (p=0.005), early diffuse SSc (p=0.008), overall limited SSc (p=0.02) and late limited SSc (p=0.03) at 1 year (but not at 2 years). The relationship was similar for severity of disease where changes in damage were related to changes in HAQ only over the first year for all 4 subgroups. CONCLUSION: The HAQ is a useful 'marker' of change in status in clinical practice, where an improved HAQ is associated with improved physician global assessment. The relationship is only helpful for an interval of one year. Low HAQ did not predict subsequent improvement by physician rating in SSc patients.
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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 | 0.003 | 0.021 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".