The health assessment questionnaire (HAQ) is strongly predictive of good outcome in early diffuse scleroderma: results from an analysis of two randomized controlled trials in early diffuse scleroderma
Bibliographic record
Abstract
OBJECTIVE: Scoring poorly on the health assessment questionnaire (HAQ) has recently been shown to be a strong predictor of morbidity and mortality in rheumatoid arthritis (RA), while a good HAQ score is predictive of a better outcome. In patients presenting with early diffuse scleroderma prognosis is variable. Our goal was to determine possible baseline predictors of future good outcomes. METHODS: We used the raw data from two randomized controlled trials (RCTs) in early diffuse scleroderma: methotrexate (Pope et al.) and D-penicillamine (Clements et al.). Subjects in the methotrexate trial were divided into the following groups: (1) those with at least 20% improvement in the primary outcome measurements [patient global assessment, physician global assessment, UCLA skin tethering score, modified Rodnan skin score (MRSS), DLCO as % predicted and HAQ disability] at 1 yr vs (2) the others. Baseline factors (including age, gender, skin scores, physician and patient global assessments, HAQ disability and pain scores, DLCO and physical parameters) were analysed to find baseline variables strongly correlated with later improvement. These variables were explored in the D-penicillamine trial to determine if (in a separate trial) they were still predictive of improved outcome at 1 and 2 yr. Adjusted models were used to find baseline predictors of good outcome. The median HAQ-DI was 1.3 (methotrexate) and 1.0 (D-penicillamine). RESULTS: A baseline HAQ disability score of less than the median was predictive of at least a 20% improvement at 1 and 2 yr with odds ratios of 1.77 to 5.05, in four of the five outcome measurements (in both groups); with strongly significant P values for 3 of 5 outcomes (UCLA skin score, MRSS, patient global skin score; P<0.02) from the methotrexate study group. These three outcomes were strongly correlated with improvement (r between 0.25 and 0.35). Although data from the D-penicillamine trial were less convincing, in both trials the less than median HAQ-DI and HAQ pain scores showed a stronger association with improved outcome, more so than age, gender, skin score and baseline global assessment. CONCLUSION: A low baseline HAQ (defined as less than the median HAQ score) is predictive of improved outcome in diffuse scleroderma at 1 and 2 yr.
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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 | 0.025 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".