The rise and fall of <scp>XMRV</scp>
Bibliographic record
Abstract
Due to the relatively recent emergence of the human T-lymphotropic and the human immunodeficiency viruses, enthusiasm for the identification of novel viruses, especially retroviruses, with pathogenic potential in humans, remains high. Novel technologies are now available with the ability to search for unknown viruses, such as gene arrays and new generation sequencing of tissue and other samples. In 2006, chip technology identified a novel retrovirus in human prostate cancer (PCa) tissue samples. Due to close homology to a mouse retrovirus, the virus was named xenotropic murine leukaemia virus-related virus (XMRV). Ever since the initial disease association with PCa, XMRV has stirred a lot of attention and concern worldwide for the medical community, public health officials and in particular global transfusion services. Public response, in this new era of electronic communication and advocacy was rapid, wide and unprecedented. In this review, we outline the course of biomedical research efforts that were put forward internationally in the process of determining the risk to the human population, the response of the blood banking community and review the current state of knowledge of xenotropic murine retroviruses. Although XMRV is no longer regarded as an infection of humans, a lesson was learnt in modern virology that holds deeper implications for biomedical research, particularly stem cell generation and transplantation practices.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".