An Ethical and Legal Analysis of Ovascience – A Publicly Traded Fertility Company and its Lead Product AUGMENT
Bibliographic record
Abstract
In May 2015, the first baby created through an emerging ova technology called AUGMENT, was born in Canada.1 Developed by OvaScience, AUGMENT essentially introduces mitochondria sourced from the genetic mother into her own ovum in order to revitalize the ovum.2 This technology is controversial because OvaScience is a publicly traded company, and it is driven by short-term results, such as earnings;3 and (2) OvaScience did not complete adequate clinical trials before offering AUGMENT to the public.4 Moreover, in its 2014 annual report, OvaScience declared that it was intentionally offering its products in countries where clinical trials are not required.5 The lack of adequate clinical trials is the primary reason underlying the Food and Drug Administration's (“FDA”) prohibition of the technology in the United States.6 Finally, as of August 2018, OvaScience has shifted its research away from AUGMENT, and it only offers it through an “exclusive license to IVF Japan Group in Japan.”7
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 | 0.026 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.012 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.019 | 0.016 |
| 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".