Adaptations of transfusion systems to the <scp>COVID</scp>‐19 pandemic in <scp>British Columbia, Canada</scp>: Early experiences of a large tertiary care center and survey of provincial activities
Bibliographic record
Abstract
BACKGROUND: In March 2020, a state of emergency was declared to facilitate organized responses to the coronavirus disease 2019 (COVID-19) pandemic in British Columbia, Canada. Emergency blood management committees (EBMCs) were formed regionally and provincially to coordinate transfusion service activities and responses to possible national blood shortages. STUDY DESIGN AND METHODS: We describe the responses of transfusion services to COVID-19 in regional health authorities in British Columbia through a collaborative survey, contingency planning meeting minutes, and policy documents, including early trends observed in blood product usage. RESULTS: Early strategic response policies were developed locally in collaboration with members of the provincial EBMC and focused on three key areas: utilization management strategies, stakeholder engagement (collaboration with frequent users of the transfusion service, advance notification of potential inventory shortage plans, and development of blood triage guidance documents), and laboratory staffing and infection control procedures. Reductions in transfusion volumes were observed beginning in mid-March 2020 for red blood cells and platelets relative to the prepandemic baseline (27% and 26% from the preceding year, respectively). There was a slow gradual return toward baseline beginning one month later; no product shortage issues were experienced. CONCLUSION: Provincial collaborative efforts facilitated the development of initiatives focused on minimizing potential COVID-19-related disruptions in transfusion services in British Columbia. While there have been no supply issues to date, the framework developed early in the pandemic should facilitate timely responses to possible disruptions in future waves of infection.
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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 | 0.000 | 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".