Profiling clinical platelet and plasma use to inform blood supply and contingency planning: PUPPY, the prospective utilization of platelets and plasma study
Bibliographic record
Abstract
BACKGROUND: Demand for platelet (PLT) and plasma transfusions is increasing. Improved clinical supply and contingency planning requires greater understanding of usage profiles and urgency of clinical requirement. STUDY DESIGN AND METHODS: This study was a random-sample survey of PLT and plasma units produced in Victoria, Australia, to determine product disposition, recipient demographics, clinical indications for transfusion, and urgency (or "deferability") of need. PLTs and fresh-frozen plasma (FFP) were tagged with a case report form before distribution. RESULTS: A total of 1252 PLT and 1837 FFP units were tagged, comprising 8.3 and 13.3% of all products issued during the study period. The fate of 1243 PLT and 1808 FFP units was determined. Of products issued, 72.2% of PLTs and 87.8% of FFP were transfused. Hematologic and oncologic disorders accounted for 63.9% of PLT transfusions, with acute myeloid leukemia alone accounting for 26%. Conversely, surgical patients received the largest proportion of FFP (40.4%), predominantly for cardiothoracic, solid organ transplant, and vascular surgery. Approximately 15% of PLT transfusions and 35% of plasma transfusions were required within 1 hour, and 80% of PLT transfusions and 90% of FFP transfusions were required within 24 hours. Wastage rates were higher in regional blood banks. CONCLUSION: The PUPPY study is a comprehensive and detailed population-based assessment of PLT and plasma usage, including urgency of use. It identifies specific clinical areas with high demand for PLT and FFP transfusion and demonstrates the high urgency of need for both products. These data inform clinical supply and contingency planning activities.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".