Quality of Care in Childhood-onset Systemic Lupus Erythematosus: Report of an Intervention to Improve Cardiovascular and Bone Health Screening
Bibliographic record
Abstract
OBJECTIVE: Initial benchmarking of childhood-onset systemic lupus erythematosus (cSLE) quality indicators revealed suboptimal performance across multiple centers. Our aim was to improve cardiovascular and bone health screenings at a tertiary treatment center for cSLE. This included annual measurements of vitamin D, lipid profiles, and bone mineral density through dual-energy x-ray absorptiometry (DXA). METHODS: Quality improvement methodology was applied to design and implement a standardized previsit planning process to electronically entered and saved orders for needed screenings prior to a scheduled clinic visit. Process outcomes were measured using statistical process control charts. Univariate analyses were completed to assess patient-level factors. RESULTS: During the study, 123 patients with cSLE participated across 619 clinic visits. The percentage of patients with completed screenings improved from 54% to 92% for annual vitamin D, 55% to 84% for annual lipid profiles, and 57% to 78% for DXA, which was sustained for more than 1 year. Providers responded to a majority of abnormal results, and improvement in the average vitamin D level was observed over time. Higher levels of disease activity, damage, number of clinic visits, and screenings completed at baseline were observed in patients with all screenings completed at the end of the intervention. CONCLUSION: Implementation of elements of the chronic illness care model for cSLE management improved performance of cardiovascular and bone health screenings, a step toward preventing longterm morbidity in cSLE. Our study also suggests that more patient interaction with the healthcare system may promote successful completion of health maintenance screenings.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".