Bibliographic record
Abstract
A 53-year-old single mother offers to donate a kidney to a work colleague whom she knows distantly. Although the recovery time needed away from work after donation will strain her modest income, the woman tells the transplant team that she understands this and is willing to go ahead. She explains that her motivation to donate is purely to help another human being. A man involved in a serious road traffic accident has suffered severe injuries and has been placed on life support while investigations are completed. The results indicate he will not survive. His relatives are not present at the hospital. The junior physician treating the patient considers withdrawing supportive treatment. He wonders whether the patient would be a candidate for non-heart-beating donation after cardiac death is pronounced. What is organ transplantation? Organ transplantation is both a life-extending and a life-saving medical procedure in which a whole or partial organ (or cells in cell therapy) from a deceased or living person is transplanted into another individual, replacing the recipient's non-functioning organ with the donor's functioning organ. Advances in the science of organ transplantation since the 1980s have significantly broadened the range of transplantable organs and improved transplant outcomes. Transplant centers in different parts of the world successfully transplant kidneys, livers, lungs, hearts, pancreases, and intestinal organs, and the procedure is considered the preferred treatment for several indications.
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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.058 | 0.020 |
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".