Hospital red blood cell and platelet supply and utilization from March to December of the first year of the <scp>COVID</scp>‐19 pandemic: The <scp>BEST</scp> collaborative study
Bibliographic record
Abstract
BACKGROUND: At the start of the coronavirus disease 2019 (COVID-19) pandemic, widespread blood shortages were anticipated. We sought to determine how hospital blood supply and blood utilization were affected by the first wave of COVID-19. STUDY DESIGN AND METHODS: Weekly red blood cell (RBC) and platelet (PLT) inventory, transfusion, and outdate data were collected from 13 institutions in the United States, Brazil, Canada, and Denmark from March 1st to December 31st of 2020 and 2019. Data from the sites were aligned based on each site's local first peak of COVID-19 cases, and data from 2020 (pandemic year) were compared with data from the corresponding period in 2019 (pre-pandemic baseline). RESULTS: RBC inventories were 3% lower in 2020 than in 2019 (680 vs. 704, p < .001) and 5% fewer RBCs were transfused per week compared to 2019 (477 vs. 501, p < .001). However, during the first COVID-19 peak, RBC and PLT inventories were higher than normal, as reflected by deviation from par, days on hand, and percent outdated. At this time, 16% fewer inpatient beds were occupied, and 43% fewer surgeries were performed compared to 2019 (p < .001). In contrast to 2019 when there was no correlation, there was, in 2020, significant negative correlations between RBC and PLT days on hand and both percentage occupancy of inpatient beds and percentage of surgeries performed. CONCLUSION: During the COVID-19 pandemic in 2020, RBC and PLT inventories remained adequate. During the first wave of cases, significant decreases in patient care activities were associated with excess RBC and PLT supplies and increased product outdating.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".