Minors as recipients and donors in solid organ transplantation
Bibliographic record
Abstract
Case narrative 1: a minor as solid organ donor A 15-year-old girl with end-stage kidney disease due to Wegener’s granulomatosis has been on dialysis since her diagnosis 18 months before. Her lung disease is quiescent. The transplant team has determined that she is ready for transplant and has identified no psychological or medical issues to delay the procedure. Each parent volunteered to be a living kidney donor, but both were blood type incompatible (they were both type A and she was a type O). The patient is highly sensitized due to transfusions received early in her illness, suggesting a long wait on the deceased-donor kidney list, even with some priority accorded children awaiting a kidney. Her fraternal twin accompanied her to a clinic visit and stated, “I want to give my sister my kidney. I’m blood type O, I know what I’m getting myself into, and I understand the risks. How do we make this happen?” Should the transplant team accept the twin’s offer and proceed with a donor evaluation? Would the situation be different if the girls were identical twins? What if the twins were 8 years old?
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".