How do I manage a blood product shortage?
Bibliographic record
Abstract
BACKGROUND: The demand for blood products sometimes exceeds the available inventory. Blood product inventories are dependent upon the availability of donors, supplies and reagents, and collection staff. During prolonged extreme shortages, blood centers and transfusion services must alter practices to meet the needs of patients. STUDY DESIGN AND METHODS: The Association for the Advancement of Blood and Biotherapies Donor and Blood Component Management Subsection compiled some strategies from its blood center and hospital transfusion service members that could be implemented during blood product shortages. RESULTS: Some strategies that blood centers could use to increase their available inventories include increasing donor recruitment efforts, using alternate types of collection kits, manufacturing low-yield apheresis-derived platelets and/or whole blood-derived platelets, using cold-stored platelets, transferring inventory internally among centers of the same enterprise, using frozen inventory, decreasing standing order quantities, prioritizing allocation to certain patient populations, filling partial orders, and educating customers and blood center staff. Transfusion service strategies that could be implemented to maximize the use of the limited available inventory include increasing patient blood management efforts, using split units, finding alternate blood suppliers, trading blood products with other hospital transfusion services, developing a patient priority list, assembling a hospital committee to decide on triaging priorities, using expired products in extreme situations, and accepting nonconforming products after performing safety checks. DISCUSSION: Blood centers and transfusion services must choose the appropriate strategies to implement based on their needs.
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 | 0.010 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".