Adolescents with renal disease in an adult world: meeting the challenge of transition of care
Bibliographic record
Abstract
A 20-year-old transplant recipient attends clinic erratically after graduating to adult care. A year after transfer she presents to emergency, weak and vomiting. Her creatinine is 900 μmol/l and she's 4 months pregnant. She admits to stopping all her immunosuppressants because ‘they’re toxic to my baby’. She loses not only her kidney but also the pregnancy. A 19-year-old transplant recipient, on the honour roll at university and a medal winner at the transplant games, participates in a mentoring program and peer support group for teenagers with renal failure. A year earlier, when he had transitioned to adult care, he was fully responsible for his medical follow-up, knowledgeable about his disease, and communicated with his nurses and physicians with clarity, confidence and a sense of humour. An 18-year-old college student on haemodialysis is transferred to an adult unit. He fails to appear for treatment. Phone calls home go unanswered. Ten days later, it is learned he died of renal failure.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.031 | 0.040 |
| Insufficient payload (model declined to judge) | 0.004 | 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".