An international effort and use of social media to save a young girl
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: A 2-year-old female with neuroblastoma needed In(b-), E- red blood cells (RBCs). No units were available at the blood centre (BC) nor in the rare donor programme member's inventories. BC's Immunohematology Reference Laboratory (IRL) and its marketing department concentrated on recruiting and testing those donors more likely to be antigen negative based on ethnicity. MATERIALS AND METHODS: The BC's communication plan utilized social and traditional media to assist in the search for In(b-) blood. Media strategies directed donors in the United States (US) and Canada to go to their nearest BC for collection, tagging and testing of units. Two segments from each donation were shipped overnight to the BC's IRL (associated with the patient) for testing. Diluted anti-Inb sera was tested by microtechniques to conserve resources. Additionally, the American Rare Donor Program (ARDP) facilitated the international searches and acted as a liaison to the Food and Drug Administration (FDA). RESULTS: More than 25 000 people responded to the appeal. Seventy-seven BCs submitted segments from 4197 units. Two donors were In(b-) but E+ and thus not compatible with the patient but were submitted to ARDP for future needs. The prevalence of In(b-) units identified in the search was 0·048%. In total, five known In(b-) donors, two from the US and three from international sources, provided units for this patient. CONCLUSION: Social media sparked a viral response to the rare blood need. While a match was not found among the units tested, domestic and international searches were able to meet the patient's blood needs.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 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".