Active seeking of post‐donation information to minimize a potential threat to transfusion safety: A pilot programme in the context of the COVID‐19 pandemic
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Early in the pandemic, the transmissibility of coronavirus disease-19 (COVID-19) by transfusion was unknown. We piloted a systematic, post-donation outreach programme to contact blood donors and inquired about symptoms post-donation. MATERIALS AND METHODS: Persons who donated on on May 1 and 2, 2020 were contacted 3 days post-donation, by phone to assess COVID-19-related symptoms. Half of the donors were administered a short questionnaire, consisting of only three questions. Others were questioned using a longer, more specific questionnaire. If symptoms were reported, products were quarantined until donors were contacted again by a trained nurse who more thoroughly assessed the likelihood of COVID-19. Blood products were withdrawn if symptoms indicative of COVID-19 were identified. RESULTS: Of 654 donors, 609 (93.1%) were successfully contacted. Of 310 donors who answered the short questionnaire and 299 who answered the long questionnaire, 19 (6.1%) and 8 (2.7%) had one or more symptoms, respectively. Based on the nurses' assessment, two donations (0.3%) had to be withdrawn. CONCLUSION: These results suggest that actively seeking post-donation information might be feasible to mitigate emerging, unqualified transfusion risks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".