Review and assessment of the donor safety among plasma donors
Bibliographic record
Abstract
Human plasma-derived medicinal products (PDMPs) are unique biological therapies derived from human plasma that are used to treat patients with rare, often genetic and chronic, conditions with a high disease burden, as well as acute indications.Despite decades of effective therapeutic use in the United States (US) and Europe and demonstrable clinical and societal value, these treatments still face numerous challenges pertaining to the plasma supply relative to growing demand, the donation landscape, regulatory and reimbursement frameworks, and treatment paradigms.1 As new indications arise and more patients are diagnosed with diseases requiring PDMP treatment, there is a growing clinical need for PDMPs, and considerably more plasma must be collected.Manufacturing from plasma collection to finished product generally takes 7 to 12 months.Plasma used for manufacturing PDMPs may be derived from whole blood donations (recovered plasma) or may be collected by apheresis for the sole purpose of fractionation (source plasma).Source plasma comprises more than 80% of the plasma fractionated in the US, totaling more than 53 million donations in 2019, with recovered plasma representing only 20%.The US
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".