Human Nuclear Genome Transfer (So‐Called Mitochondrial Replacement): Clearing the Underbrush
Bibliographic record
Abstract
In this article, I argue that there is no compelling therapeutic 'need' for human nuclear genome transfer (so-called mitochondrial replacement) to prevent mitochondrial diseases caused by mtDNA mutations. At most there is a strong interest in (i.e. 'want' for) this technology on the part of some women and couples at risk of having children with mitochondrial disease, and perhaps also a 'want' on the part of some researchers who see the technology as a useful precedent - one that provides them with 'a quiet way station' in which to refine the micromanipulations techniques essential for other human germline interventions and human cloning. In advance of this argument, I review basic information about mitochondrial disease and novel genetic strategies to prevent the transmission of mutated mitochondria. Next, I address common features of contemporary debates and discussions about so-called mitochondrial replacement. First, I contest the cliché that science-and-(bio)technology is fast outpacing ethics. Second, I dispute the accuracy of the term 'mitochondrial replacement'. Third, I provide a sustained critique of the purported 'need' for genetically-related children. In closing, I call into question the mainly liberal defense of human nuclear genome transfer. I suggest an alternative frame of reference that pays particular attention to issues of social justice. I conclude that our limited resources (time, talent, human eggs, and money) should be carefully expended in pursuit of the common good, which does not include pandering to acquired desires (i.e., wants).
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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 | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.056 |
| Scholarly communication | 0.007 | 0.017 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.012 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".