Triage tool for the rationing of blood for massively bleeding patients during a severe national blood shortage: guidance from the National Blood Transfusion Committee
Bibliographic record
Abstract
The emerging COVID-19 pandemic has overwhelmed healthcare resources worldwide, and for transfusion services this could potentially result in rapid imbalance between supply and demand due to a severe shortage of blood donors. This may result in insufficient blood components to meet every patient's needs resulting in difficult decisions about which patients with major bleeding do and do not receive active transfusion support. This document, which was prepared on behalf of the National Blood Transfusion Committee in England, provides a framework and triage tool to guide the allocation of blood for patients with massive haemorrhage during severe blood shortage. Its goal is to provide blood transfusions in an ethical, fair, and transparent way to ensure that the greatest number of life years are saved. It is based on an evidence- and ethics-based Canadian framework, and would become operational where demand for blood greatly exceeds supply, and where all measures to manage supply and demand have been exhausted. The guidance complements existing national shortage plans for red cells and platelets.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.031 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.012 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 0.009 |
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".