Anonymous Living Liver Donation: Donor Profiles and Outcomes
Bibliographic record
Abstract
There are no published series of the assessment process, profiles and outcomes of anonymous, directed or nondirected live liver donation. The outcomes of 29 consecutive potential anonymous liver donors at our center were assessed. We used our standard live liver assessment process, augmented with the following additional acceptance criteria: a logical rationale for donation, a history of social altruism, strong social supports and a willingness to maintain confidentiality of patient information. Seventeen potential donors were rejected and 12 donors were ultimately accepted (six male, six female). All donors were strongly motivated by a desire and sense of responsibility to help others. Four donations were directed toward recipients who undertook media appeals. The donor operations included five left lateral segmentectomies and seven right hepatectomies. The overall donor morbidity was 40% with one patient having a transient Clavien level 3 complication (a pneumothorax). All donors are currently well. None expressed regret about their decision to donate, and all volunteered the opinion that donation had improved their lives. The standard live liver donor assessment process plus our additional requirements appears to provide a robust assessment process for the selection of anonymous live liver donors. Acceptance of anonymous donors enlarges the donor liver pool.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".