1 ELSEVIER High-Risk Donors: Expanding Donor Criteria
Bibliographic record
Abstract
of the cadaveric donor pool has necessitated a re-evaluation of donor criteria to include organs that were previously considered high risk. The number of patients on the waiting list for all organs has since increased by 22 % per year so that by the end of 1995, a total of 44,000 patients were registered (13,000 extrarenal and 31,000 renal).1 Con-currently, deaths on the waiting list have increased by 18% per year while the total number of transplantations has only increased by 8 % per year. In 1996, 13,000 kidney trans-plants were performed in the United States and 934 in Canada. Simultaneously, the number of cadaveric organ donors has remained relatively static, with only a 4% increase per year from 1988 to 1994. Most of this incremen-tally small increase has been through the use of "expanded" donors, reflected by the fact that the use of donors older
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.054 | 0.009 |
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".