Transfer from Pediatric to Adult Care Is Hardly Child’s Play
Bibliographic record
Abstract
In this issue of The Journal , Chang and colleagues describe patterns of rheumatologic/nephrologic care and changes in healthcare use and medication adherence among patients with childhood-onset systemic lupus erythematosus (SLE) during their transfer from pediatric to adult care1. The most salient of their findings was a 26% rate of loss to follow-up, defined as more than 12 months between last pediatric visit and first adult visit, or end of enrollment. Consequently, and not surprisingly, ambulatory visits declined (although interestingly, there was no signal for increased acute care use). Medication adherence was poor across groups, regardless of whether the transfer was deemed a “success.” Taken together, Chang, et al ’s findings reinforce the overarching themes of the literature on pediatric transition to adult care, not to mention the anecdotal — but remarkably consistently held — impressions of physicians who practice in this space: that the period of transition from pediatric to adult care is littered with landmines, quilted with quagmires. Some find their way to adult care and succeed; many others, however, fail dramatically and with dire consequences. Other groups have reported that only 50% of pediatric rheumatology patients transition successfully to adult care2,3. Surely, we could forge a less perilous path. And while patients with childhood-onset SLE, like those in the Chang study, reflect a particularly vulnerable group, with complex and potentially fatal multisystemic disease, the challenges associated with transfer from pediatric to adult care transcend diagnosis or specialty. Themes are consistent among chronic diseases of onset in childhood that persist into adulthood regardless of whether the young patient has SLE, juvenile idiopathic arthritis (JIA), diabetes, or sickle cell disease4,5,6. The unique challenges associated with care of these patients are … Address correspondence to Dr. A. Steiman, Sinai Health System, Medicine, 60 Murray St., Suite 2-223, Toronto, ON M5T 3L9, Canada. Email: amanda.steiman{at}sinaihealthsystem.ca.
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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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.009 | 0.017 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".