Factors Associated With Variation in Pediatric Systemic Lupus Erythematosus Care Delivery
Bibliographic record
Abstract
OBJECTIVE: Patients with pediatric systemic lupus erythematosus (pSLE) and mixed connective tissue disease (MCTD) receive only a fraction of recommended care. Using published quality indicators and guidelines, we developed a 13-item pediatric lupus care index (p-LuCI) to quantify the proportion of recommended clinical evaluations and comorbidity prevention interventions completed and the timeliness of follow-up. Our objective was to assess baseline index performance and identify sources of p-LuCI variation. METHODS: We performed a cross-sectional study in patients with pSLE or MCTD and analyzed the performance of individual p-LuCI process metrics and calculated the overall p-LuCI score. We identified factors associated with the p-LuCI using multivariable linear regression with clustering by provider. RESULTS: For 110 patients (99 with pSLE and 11 with MCTD), the median p-LuCI was 65.2% (interquartile range: 9.1-92.3%). Component performance ranged from 27.3% (on-time scheduling) to 95.4% (steroid-sparing treatment). Patients with p-LuCI scores above the median had higher scores across all 13 components. Higher p-LuCI scores were independently associated with disease-modifying antirheumatic drug use (β = 14.3 [95% confidence interval (CI), 1.5-27.2]), nephritis (β = 10.4 [95% CI, 5.1-15.8]), higher provider pSLE/MCTD volume (β = 3.1 [95% CI, 1.9-4.2] per patient), assignment to rheumatology fellow trainee (β = 36.3 [95% CI, 17.3-55.2]), and disease duration of less than 1 year (β = 12.6 [95% CI, 0.7-24.5]). Differences by race, ethnicity, and/or insurance were not observed. CONCLUSION: Using an index of recommended pSLE care metrics, we identified significant variation in performance by disease, treatment, and provider characteristics. The p-LuCI may be useful to assess care quality at the patient, provider, and practice levels and to identify areas in need of greater standardization.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".