Evaluating the inventory impact of utilizing low titer platelets in regional hospitals
Bibliographic record
Abstract
BACKGROUND: As a result of constrained supply, it is sometimes necessary to provide patients with ABO-mismatched platelets. Such practices increase the risk of acute hemolytic transfusion reaction (AHTR). Providing patients with platelets suspended in O plasma having low-titer Anti-A and Anti-B antibodies (LtABO) could reduce the incidence of AHTR. However, natural scarcity limits the number of such units that can be produced. In this paper we present a study to evaluate strategies for deploying LtABO at regional hospitals in Canada. STUDY DESIGN AND METHODS: Regional hospitals often experience demand for platelets on an irregular basis. They are, however, required to stock some number of platelets (typically one A-unit and one O-unit) for emergencies; outdates are common, with discard rates sometimes >>50%. A simulation study was completed to determine the impact of replacing a (1A, 1O) inventory with 2 or 3 units of LtABO at regional hospitals. RESULTS: A significant decreases in wastage and shortage can be expected by replacing a (1A, 1O) inventory policy with 2 units of LtABO. In tested cases, a 2-unit LtABO dominated a (1A, 1O) policy, resulting in statistically fewer outdates and instances of shortage. Holding 3 units of LtABO, increases product availability, but results in an increase in outdates when compared to a (1A, 1O) policy. CONCLUSION: Providing LtABO platelets to smaller, regional hospitals will lower wastage rates and improve patient access to care, when compared to existing (1A, 1O) inventory policies.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".