Blood donor notification of variant Creutzfeldt–Jakob disease risk: Lessons in communicating donor deferral and risk
Bibliographic record
Abstract
BACKGROUND: In 2005, the blood service in England notified 101 donors by letter that they may be at risk of variant Creutzfeldt-Jakob disease (vCJD) because a recipient of their blood later developed vCJD. Donor experience of the notification was studied in a 2009 survey. METHODS: Fifteen questions focused on satisfaction, emotional response and understanding of the notification letter. An average Likert score was calculated: 1 and 2 = dissatisfied, 3 = equivocal and 4 and 5 = satisfied; the per cent satisfied and dissatisfied were calculated and characteristics compared using the Fisher and Chi-squared tests. RESULTS: The questionnaire was completed by 56 of 90 notified donors, mostly repeat, U.K.-born donors over 45 years of age. Four years after notification, many individuals still felt surprise (44%), upset (44%) or worry (50%) about the letter, with 10 feeling depressed. Thirty per cent were uncertain if they had vCJD or not. For future notifications, 57% would still favour a detailed letter and 36% would prefer a discussion in person. DISCUSSION: It was notable how many individuals, 4 years later, still felt continuing anxiety about the vCJD notification letter, not noted in earlier interviews. This highlights a need for on-going support required in donor notifications where outcome for the individual is highly uncertain.
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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 | 0.018 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".