Bibliographic record
Abstract
PURPOSE OF REVIEW: Pediatric solid organ transplantation numbers have been increasing over the years. Research and the medical literature tends to focus on advancing the field and innovation - which often leads to higher risk and more complex procedures. How do we decide when it is too much - too much risk; too much uncertainty? Who makes that decision? Literature is scarce and usually focuses on end-of-life decision-making. This article does not purport to have the answers, but will highlight the depth and breadth of points that must be taken into consideration. RECENT FINDINGS: There are many factors that contribute to the decision-making in the context of high-risk solid organ transplantation in children. Focus needs to include quality of life in the pediatric context, in addition to survival. End-of-life discussions should be included early in the process. Societal factors must be considered in an era of donor organ shortages. Shared decision-making should be the approach. SUMMARY: The key guiding principle is to make a decision about what is best for a child requiring a high-risk transplant based not only on survival, but also on an acceptable quality of life on the background of optimal utilization of a scarce societal resource.
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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".