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.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".