What Factors Influence the Health Status of Patients with Rheumatoid Arthritis Measured by the SF-12v2 Health Survey and the Health Assessment Questionnaire?
Bibliographic record
Abstract
OBJECTIVE: The Health Assessment Questionnaire Disability Index (HAQ) is a widely used outcome measure in rheumatoid arthritis (RA), whereas the SF-12v2 Health Survey (SF-12) was introduced recently. We investigated how the HAQ and SF-12 were associated with socio-demographic, lifestyle, and disease- and treatment-related factors in patients with RA. METHODS: In RA patients from 11 Danish centers, clinical and patient-reported data, including the HAQ and SF-12, were collected. Three multiple linear regression models were estimated, with the HAQ, SF-12 physical component score (PCS), and SF-12 mental component score (MCS) as outcome and sociodemographic, lifestyle, and RA-related treatment and comorbidity characteristics as explanatory variables. RESULTS: In total, 3156 (85%) of 3704 invited patients participated--75% women, 76% rheumatoid factor-positive, median age 61 years (range 15-93 yrs), disease duration 7 years (range 0-68 yrs), Disease Activity Score on 28 joints (DAS28) 2.97 (range 0.96-8.61), HAQ score 0.63 (range 0-3), SF-12 PCS 56 (range 6-99), and SF-12 MCS 57 (range 16-99). Variation in HAQ was associated with 12 of 15 possible variables (R(2) 0.41), in PCS and MCS with 6 of 15 variables (R(2) 0.02 and 0.05). Patients with moderate to high DAS28 and > or = 3 comorbid conditions had consistently worse HAQ and SF-12 scores compared to the reference groups, while weekly exercise was associated with better scores compared to no exercise. CONCLUSION: The HAQ was more sensitive to differences in demographic, lifestyle, and disease- and treatment-related factors than the SF-12. The established clinical value and feasibility of the HAQ highlights its advantages over the SF-12 in describing health status in RA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.002 | 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".