Bibliographic record
Abstract
Since the early 1990s, investigators have toiled to establish the transfer of genes to human somatic cells as a valid therapy (fig 1). The potential of gene transfer was highlighted by three trials, involving participants with haemophilia B and two types of severe combined immunodeficiency—X linked and adenosine deaminase deficient.1–3 Yet even the most successful trials of gene transfer have engendered questions about its prospects. In the haemophilia B trial the detection of vector—the agent which carries genes to cells (fig 2)—in participants' semen raised concerns about modifications of the germline.4 The results of the X linked severe combined immunodeficiency trial were offset by unexpected, vector induced leukaemia in two participants.5 Fig 1 Number of gene transfer trials approved worldwide has increased since 1989; 77% have been conducted in the United States and the United Kingdom. Most trials have used virus based vectors (70%), are phase 1 (63%), and involve investigational treatments for cancer (66%). Adapted from Wiley Gene Therapy Clinical Trial Database (http://www.wiley.co.uk/wileychi/genmed/clinical/) Fig 2 In a gene transfer, therapeutic DNA is combined with a vector (often of viral origin). Vectors can be injected into recipient's tissue directly or used to modify cells ex vivo for transplantation to the recipient Several hazards associated with gene transfer have been verified by clinical experience and others are predicted on theoretical grounds. It therefore may be worth considering whether risks shown in studies of human gene transfer present any unusual ethical and social challenges and, if so, what should be done to tackle them. In this article I review several matters relating to human gene transfer—safety features that distinguish traditional drugs from agents used to transfer genes, ethical issues raised by uncertainties about risk and toxicological properties, and studies on safety. I searched for relevant articles in Medline through PubMed …
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 | 0.053 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.021 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.014 | 0.017 |
| Insufficient payload (model declined to judge) | 0.007 | 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".