The impact of the human genome project clinical care and ethical challenges
Bibliographic record
Abstract
June 2000 heralded the first draft of the human genome and with it a tremendous amount of public attention to this monumental achievement Amidst the excitement regarding the potential impact on clinical medicine and anticipating uthe development of rational strategies for minimizing or preventing disease phenotypes altogether” there has also arisen concern about the ethical use of this new technology as well as a healthy dose of skepticism about its ultimate application to clinical care. Clearly, as technology continues to elucidate new genes and theirfunction, both in normal development and pathology, questions and concerns about practical applications of this information will continue to arise. The goal of this article is to review the impact of the discoveries of Human Genome Project (HGP) on the practice of genetic counselling and clinical care as well as to discuss the potential ethical dilemmas, which may arise.
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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 | 0.080 | 0.110 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.022 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.017 | 0.019 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".