A <scp>data‐informed</scp> system to manage scarce blood product allocation in a randomized controlled trial of convalescent plasma
Bibliographic record
Abstract
BACKGROUND: Equitable allocation of scarce blood products needed for a randomized controlled trial (RCT) is a complex decision-making process within the blood supply chain. Strategies to improve resource allocation in this setting are lacking. METHODS: We designed a custom-made, computerized system to manage the inventory and allocation of COVID-19 convalescent plasma (CCP) in a multi-site RCT, CONCOR-1. A hub-and-spoke distribution model enabled real-time inventory monitoring and assignment for randomization. A live CCP inventory system using REDCap was programmed for spoke sites to reserve, assign, and order CCP from hospital hubs. A data-driven mixed-integer programming model with supply and demand forecasting was developed to guide the equitable allocation of CCP at hubs across Canada (excluding Québec). RESULTS: 18/38 hospital study sites were hubs with a median of 2 spoke sites per hub. A total of 394.5 500-ml doses of CCP were distributed; 349.5 (88.6%) doses were transfused; 9.5 (2.4%) were wasted due to mechanical damage sustained to the blood bags; 35.5 (9.0%) were unused at the end of the trial. Due to supply shortages, 53/394.5 (13.4%) doses were imported from Héma-Québec to Canadian Blood Services (CBS), and 125 (31.7%) were transferred between CBS regional distribution centers to meet demand. 137/349.5 (39.2%) and 212.5 (60.8%) doses were transfused at hubs and spoke sites, respectively. The mean percentages of total unmet demand were similar across the hubs, indicating equitable allocation, using our model. CONCLUSION: Computerized tools can provide efficient and immediate solutions for equitable allocation decisions of scarce blood products in RCTs.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".