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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".