Disclosure of Misattributed Paternity: Issues Involved in the Discovery of Unsought Information
Bibliographic record
Abstract
Kidney transplantation from living donors is generally a safe, effective form of renal replacement therapy. When evaluating potential living donors and their intended recipients, a careful assessment process is followed in order to ensure that ethical standards are upheld. During this assessment, important medical information with serious consequences, which was not being sought as part of the donor/recipient evaluation, may be discovered. The information may or may not be relevant to the decision to donate. However, such a discovery raises the difficult questions of whether or not there is an obligation to disclose the information, to whom does the information belong, and what process should be used to resolve the issue? We present a case that forced us to confront these questions and raised issues of truth telling, autonomy, paternalism, confidentiality, and the nature of the relationship between patients and health care professionals.
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 | 0.068 | 0.152 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.033 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.019 | 0.016 |
| Insufficient payload (model declined to judge) | 0.002 | 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".