U.S. cities will not meet blood product resuscitation standards during major mass casualty incidents: Results of a <scp>THOR‐AABB</scp> working party prospective analysis
Bibliographic record
Abstract
BACKGROUND: Mass casualty incidents (MCIs) create an immediate surge in blood product demand. We hypothesize local inventories in major U.S. cities would not meet this demand. STUDY DESIGN AND METHODS: A simulated blast in a large crowd estimated casualty numbers. Ideal resuscitation was defined as equal amounts of red blood cells (RBCs), plasma, platelets, and cryoprecipitate. Inventory was prospectively collected from six major U.S. cities at six time points between January and July 2019. City-wide blood inventories were classified as READY (>1 U/injured survivor), DEFICIENT (<10 U/severely injured survivor), or RISK (between READY and DEFICIENT), before and after resupply from local distribution centers (DC), and features of DEFICIENT cities were identified. RESULTS: The simulated blast resulted in 2218 injured survivors including 95 with severe injuries. Balanced resuscitation would require between 950 and 2218 units each RBC, plasma, platelets and cryoprecipitate. Inventories in 88 hospitals/health systems and 10 DCs were assessed. Of 36 city-wide surveys, RISK inventories included RBCs (n = 16; 44%), plasma (n = 24; 67%), platelets (n = 6; 17%), and cryoprecipitate (n = 22; 61%) while DEFICIENT inventories included platelets (n = 30; 83%) and cryoprecipitate (n = 12; 33%). Resupply shifted most RBC and plasma inventories to READY, but some platelet and cryoprecipitate inventories remained at RISK (n = 24; 67% and n = 12; 33%, respectively) or even DEFICIENT (n = 11; 31% and n = 6; 17%, respectively). Cities with DEFICIENT inventories were smaller (p <.001) with fewer blood products per trauma bed (p <.001). DISCUSSION: In this simulated blast event, blood product demand exceeded local supply in some major U.S. cities. Options for closing this gap should be explored to optimize resuscitation during MCIs.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".