Frailty and Associated Outcomes in Patients With Vasculitis
Bibliographic record
Abstract
OBJECTIVE: To describe the frequency and outcomes associated with self-reported frailty in patients with vasculitis. METHODS: VascStrong is a longitudinal study using the Vasculitis Patient-Powered Research Network, an internet-based cohort of patients with vasculitis. Data collected included patient global assessment (PtGA) and several domains of the Patient-Reported Outcomes Measurement Information System (PROMIS). Frailty was measured at baseline and 1-year follow-up using the FRAIL scale, a 5-domain self-reported measure. Patients were classified as nonfrail, prefrail, and frail based on 0, 1-2, or ≥ 3 criteria, respectively. At follow-up, patients reported the occurrence over the prior year of hospitalizations, infections, fractures, and disease flares. A multivariable logistic regression was performed to identify factors associated with frailty. RESULTS: The baseline survey included 328 patients. Patients had a mean age of 59.5 years, were predominantly female (71.6%) and non-Hispanic White. Prevalence of prefrailty and frailty was 42.1% and 21.6%, respectively. The majority of patients with each form of vasculitis were classified as frail or prefrail. Prefrail and frail patients reported worse PROMIS scores at baseline and follow-up. Frailty was independently associated with female sex, higher PtGA scores, being overweight, and obesity, but not with age. At 1 year, 272 of 328 (82.9%) patients answered the follow-up survey. Transitions in frailty status were observed in 99 (36.4%) patients. Hospitalizations and flares were more frequent in frail patients. CONCLUSION: Self-reported frailty or prefrailty is common in the majority of patients with multiple forms of vasculitis, indicating there is a substantial subset of patients at risk for worse outcomes.
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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 | 0.004 |
| 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 | 0.001 |
| 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".