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 Blood products were withdrawn if symptoms indicative of COVID-19 were identified.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 Based on the nurses' assessment, two donations (0.3%) had to be withdrawn.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 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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".