What is a cure and how do we get there?
Bibliographic record
Abstract
The absence of adequate treatment for most of the world's 400 000 individuals with haemophilia makes the development of a cure compelling. Advances in the basic molecular sciences over the past 20 years have resulted in the feasibility of curing haemophilia through the application of gene therapy. However, the reality of this therapeutic strategy is highly complex. In addition, challenges to achieving a cure exist beyond the basic scientific hurdles. Thoughtful attention must also be given to a number of interrelated issues, including ethical considerations in patient recruitment, informed consent and geographical variables of global clinical trials. The global inequalities in healthcare mean that the ethics of international medical research, especially when it includes countries where people usually do not receive quality care, become much more complicated. The majority of haemophiliacs lives in developing countries and is a valuable resource of human subjects who could be enrolled in clinical trials. When recruiting subjects globally, investigators must be ever mindful that the patient population is a precious resource, which must be treated with respect and care. This presents a major challenge for investigators engaged in trials of haemophilia gene therapy to ensure that the informed consent process is current and comprehensive, that therapeutic misconceptions are appropriately managed, and that the roles of the researcher and physician are clear. Global clinical gene-therapy trials are an important and appropriate component in the quest to achieve a cure for haemophilia. When trials follow identical internationally accepted standards, a successful outcome can be achieved for trials including developing countries, if country specific cultural and economic aspects are considered.
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 | 0.019 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.021 |
| Scholarly communication | 0.013 | 0.023 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.012 | 0.018 |
| Insufficient payload (model declined to judge) | 0.017 | 0.012 |
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".