Bibliographic record
Abstract
What Is the Issue? The first therapeutics based on clustered regularly interspaced short palindromic repeats (CRISPR) technologies are entering the market. These gene-editing technologies have the potential to change treatment paradigms and may be used to treat conditions that cannot be treated or cured with current methods. This report aims to provide an overview of these technologies and their current and potential roles in health care. What Is the Technology? CRISPR is a novel and emerging technology, first discovered in bacterial immune systems, that can cut DNA strands and be used as a gene-editing tool. A guide ribonucleic acid (RNA) sequence leads the CRISPR-associated nuclease to the target DNA sequence where the cut is made. These edits change the function of the gene, making genes nonfunctional or replacing the coding sequence for 1 gene with another. CRISPR can also be used to increase or decrease the expression of specific genes. What Is the Potential Impact? CRISPR-based technologies have a variety of potential applications in health care, including: treating genetic diseases understanding the genetic mechanisms of diseases and investigating the relevance of potential drug treatments managing infectious diseases through detection, treatment, and elimination. What Else Do We Need to Know? The long-term effects of CRISPR-based therapies are currently unknown. While the first of these therapies, exagamglogene autotemcel (exa-cel) (Casgevy), is the first and only CRISPR-based therapy to receive regulatory approval anywhere internationally (e.g., US, UK), as well as in Canada in September 2024, the next viable CRISPR-based therapies are still in development, with the pivotal clinical trials not expected to be completed until at least 2027. Several ethical considerations related to the use of CRISPR-based therapies have been identified, including the implications of off-target gene modifications, a need for robust informed consent processes, and a need for ethical and legal guidelines.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.007 |
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".