Buffy coat platelets coming to <scp>America</scp>: Are we ready?
Bibliographic record
Abstract
BACKGROUND: Buffy coat (BC) platelets (PLTs) have been used globally for many years. In 2004 Canadian Blood Services (CBS) made the decision to transition from PLT-rich plasma (PRP) to BC PLTs. We reviewed the benefits and manufacture process of BC and the implementation challenges involved. STUDY DESIGN AND METHODS: A literature review was performed in the following areas: BC efficacy, donor population shifts, production and good stewardship of PLTs, logistic considerations with overnight holds, advantages of the overnight hold, the CBS experience, licensure and standards, and changes needed to produce BC PLTs in the United States. The aim was to analyze current practice and identify possible actions for blood centers and hospitals. RESULTS: Implementation of BC would offer an additional source of PLTs to address the growing elderly population and the declining apheresis donor base. Substantial logistic, operational, and financial benefits were seen when CBS transitioned to BC with overnight hold. CONCLUSIONS: Buffy coat blood products are widely used throughout the world. Recent conversion from PRP to BC by CBS showed that conversion can be accomplished with planning, communication, and partnership from all stakeholders. In conclusion, BC PLTs are worth serious consideration in the United States, but regulatory barriers in the United States will need to be addressed.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".