Patterns of Healthcare Use and Medication Adherence among Youth with Systemic Lupus Erythematosus during Transfer from Pediatric to Adult Care
Bibliographic record
Abstract
OBJECTIVE: Youth with systemic lupus erythematosus (SLE) transferring from pediatric to adult care are at risk for poor outcomes. We describe patterns of rheumatology/nephrology care and changes in healthcare use and medication adherence during transfer. METHODS: We identified youth ages 15-25 with SLE using US private insurance claims from Optum's deidentified Clinformatics Data Mart. Rheumatology/nephrology visit patterns were categorized as (1) unilateral transfers to adult care within 12 months, (2) overlapping pediatric and adult visits, (3) lost to followup, or (4) continuing pediatric care. We used negative binomial regression and paired t tests to estimate changes in healthcare use and medication possession ratios (MPR) after the last pediatric (index) visit. We compared MPR between youth who transferred and age-matched peers continuing pediatric care. RESULTS: Of the 184 youth transferred out of pediatric care, 41.8% transferred unilaterally, 31.5% had overlapping visits over a median of 12 months before final transfer, and 26.6% were lost to followup. We matched 107 youth continuing pediatric care. Overall, ambulatory care use decreased among those lost to followup. Acute care use decreased across all groups. MPR after the index date were lower in youth lost to followup (mean 0.24) compared to peers in pediatric care (mean 0.57, p < 0.001). CONCLUSION: Youth with SLE with continuous private insurance coverage do not use more acute care after transfer to adult care. However, a substantial proportion fail to see adult subspecialists within 12 months and have worse medication adherence, placing them at higher risk for adverse 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 | 0.000 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".