The epidemiology of human T‐cell lymphotropic virus types I and <scp>II</scp> in Canadian blood donors
Bibliographic record
Abstract
OBJECTIVES: Blood donors in Canada have been tested for Human T-Cell Lymphotropic Virus (HTLV) since 1990. We report the epidemiology, risk factors and lookback/traceback of HTLV-positive donors/recipients. METHODS: The annual HTLV rate was calculated from 1990 to 2010. Residual risk was estimated as the product of incidence and window period. Twenty-nine HTLV-positive donors and 116 matched controls (ratio 1 : 4) were interviewed about risk factors. For HTLV-positive donations, lookback investigations involved identification of all previous donations, and attempting to locate and test recipients. Traceback was initiated when transfusion transmission was queried for HTLV-positive blood recipients. All donors of products that the recipient received were identified, with an attempt to locate and test them. RESULTS: The HTLV rate decreased from 9.35 per 100,000 donations in 1990 to 1.11 in 2010. The residual risk of infection was 1 in 7.6 million donations. In logistic regression birth overseas (OR 18.7), history of sexually transmitted diseases (OR 32.9), sex with unknown background (OR 5.4) and blood transfusion (OR 8.9) were significant predictors. In the lookback study, of 109 HTLV-positive donors, 508 components were transfused, of whom 147 recipients were tested and 18 (12%) were positive. All were transfused prior to the implementation of donor testing. Twenty-three traceback investigations were requested involving 324 transfused untested products,of whom 219 (67.6%) of donors were tested and 13 (6%) were positive for HTLV. CONCLUSIONS: With testing of the blood supply, the risk from HTLV is very low and while most HTLV-positive donors have risk factors, deferrable risk is rare.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".