The frequency of and associations with hospitalization secondary to lupus flares from the 1000 Faces of Lupus Canadian cohort
Bibliographic record
Abstract
OBJECTIVES: Hospitalization is a major factor in health care costs and a surrogate for worse outcomes in chronic disease. The aim of this study was to determine the frequency of hospitalization secondary to lupus flare, the causes of hospitalization, and to determine risk factors for hospitalization in patients with systemic lupus erythematosus (SLE). METHODS: Data were collected as part of the 1000 Canadian Faces of Lupus, a prospective cohort study, where annual major lupus flares including hospitalizations were recorded over a 3-year period. RESULTS: Of 665 patients with available hospitalization histories, 68 reported hospitalization related to a SLE flare over 3 years of follow-up. The average annual hospitalization rate was 7.6% (range 6.6-8.9%). The most common reasons for hospitalization were: hematologic (22.1%), serositis (20.6%), musculoskeletal (MSK) (16.2%), and renal (14.7%). Univariate risk factors for lupus hospitalization included (OR [95% CI]; p < 0.05): juvenile-onset lupus (2.2 [1.1-4.7]), number of ACR SLE criteria (1.4 [1.1-1.7], baseline body mass index (BMI) (1.1 [1.0-1.1]), psychosis (3.4 [1.2-9.9]), aboriginal race (3.2 [1.5-6.7]), anti-Smith (2.6 [1.2-5.4]), erythrocyte sedimentation rate >25 mm/hr (1.9 [1.1-3.4]), proteinuria >0.5 g/d (4.2 [1.9-9.3], and SLAM-2 score (1.1 [1.0-1.2]). After multivariate regression only BMI, number of ACR criteria, and psychosis were associated with hospitalization for lupus flare. CONCLUSIONS: The mean annual rate of hospitalization attributed to lupus was lower than expected. Hematologic, serositis, MSK and renal were the most common reasons. In a regression model elevated BMI, more ACR criteria and psychosis were associated with hospitalization.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".