Mortality Risk by Functional Status and Health-related Quality of Life in Patients with Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: Patients with rheumatoid arthritis (RA) are at increased risk of death. Modern RA therapy has been shown to improve health status, but the relationship of such improvements to mortality risk is unknown. We assessed the relationship between health status and all-cause mortality in patients with RA, using the Health Assessment Questionnaire (HAQ) and the Medical Outcomes Study Short Form-36 questionnaire (SF-36) physical and mental component summary scores (PCS, MCS). METHODS: Subjects (n = 10,319) were selected from the National Data Bank for Rheumatic Diseases, a prospective longitudinal observational US study with semiannual assessments of HAQ, PCS, and MCS. Risk of death up to 7 years through 2006 was obtained from the US National Death Index. Relationship of HAQ, PCS, and MCS to mortality was assessed using Cox regression models; prediction accuracy was compared using Harrell's concordance coefficient (C). RESULTS: Over 64,888 patient-years of followup, there were 1317 deaths. Poorer baseline health status was associated with greater mortality risk. Adjusting for age, sex, and baseline PCS and MCS, declines in PCS and HAQ were associated with higher risk of death. HAQ improvement was associated with reduced mortality risk from 6 months through 3 years; a similar relationship was not observed for PCS or MCS improvement. Controlling for baseline values, change in PCS or HAQ did not improve prediction accuracy. CONCLUSION: The HAQ and the SF-36 PCS are similarly and strongly associated with mortality risk in patients with RA. Change in these measures over time does not appear to add to predictive accuracy over baseline levels.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".