Human T-Lymphotropic Virus Screening of Blood Donations in England Between 2002 and 2021—Comparison of Screening Strategies
Bibliographic record
Abstract
BACKGROUND: Human T-lymphotropic virus (HTLV) is associated with adult T-cell leukemia/lymphoma and myelopathy. Here we present virological and epidemiological data on HTLV screening of blood donations in England between 2002 and 2021, implemented to prevent its transmission via blood transfusion. METHODS: Data on HTLV testing of blood donations was reviewed; it was initially conducted in pools (2002-2012) and subsequently using individual samples (all donors, 2013-2016; first-time donors and non-leucodepleted component donors, 2017-2021). Data included annual number of donations screened, initial and repeat reactives as well as confirmed positives. Further information, such as likely source of infection, was obtained for HTLV-positives. RESULTS: Over the 20-year study period, a total of 30 679 741 blood donations were screened for HTLV in England. Under pooled screening strategy, the annual rate of repeat reactive donations remained <5:100 000. However, this rate increased to 51:100 000 with individual screening and further to 123:100 000 with selective screening. A total of 5032 samples were repeat reactive, of which 278 were confirmed HTLV-positives. Although the specificity under each scenario exceeded 99.9%, the rate of repeat reactives was around 50-fold higher in individual compared to pooled screening. Most HTLV infected were UK-born, most likely acquired their infection unknowingly through breast feeding or heterosexual intercourse with an individual associated with an HTLV-endemic country. CONCLUSIONS: These data highlight that pooled testing can be advantageous in low-prevalence settings due to its high specificity and reduced non-specific reactivity. Whether pooling is an applicable strategy to tackle the burden of HTLV infection in resource-poor, HTLV-endemic countries requires further investigations.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".