Living Kidney Donor Follow-Up: State-of-the-Art and Future Directions, Conference Summary and Recommendations
Bibliographic record
Abstract
In light of continued uncertainty regarding postkidney donation medical, psychosocial and socioeconomic outcomes for traditional living donors and especially for donors meeting more relaxed acceptance criteria, a meeting was held in September 2010 to (1) review limitations of existing data on outcomes of living kidney donors; (2) assess and define the need for long-term follow-up of living kidney donors; (3) identify the potential system requirements, infrastructure and costs of long-term follow-up for living kidney donor outcomes in the United States and (4) explore practical options for future development and funding of United States living kidney donor data collection, metrics and endpoints. Conference participants included prior kidney donors, physicians, surgeons, medical ethicists, social scientists, donor coordinators, social workers, independent donor advocates and representatives of payer organizations and the federal government. The findings and recommendations generated at this meeting are presented.
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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 | 0.058 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".