Bibliographic record
Abstract
Continuous improvement in blood bankingThroughout the world, blood services aim to provide a lifesaving service by ensuring an adequate supply of safe, high-quality blood products.In addition to a critical focus on donor recruitment and testing, haemovigilance and overall blood system management, a continuous effort is in place to improve the quality of the products that are prepared.In order to make quantum improvements to blood products, it is necessary to thoroughly understand the components themselves.While production processes are associated with alterations in red cell [1] or platelet products [2,3], the full spectrum of changes is not yet well-understood.New developments in analytical science provide new tools to explore fundamental problems facing transfusion medicine.Proteomics is one such tool that affords a new examination of these questions.Herein, we summarize the current state of the application of proteomics to the challenges in transfusion medicine, both at the donor assessment level and at the level of component preparation and quality.Others have come before us, and the reader is referred to additional descriptions of the recent advances in the application of proteomics in transfusion medicine summarized in these excellent review articles [4 -7].
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".