Changes in plasma unit distributions to hospitals over a 10‐year period
Bibliographic record
Abstract
BACKGROUND: There are many influences on a hospital's demand for plasma. Pharmaceuticals are now being administered for many indications instead of plasma, although trauma resuscitation now emphasizes increased and early intervention with plasma. This multinational study evaluated changes in blood center plasma unit distributions over a 10-year period. STUDY DESIGN AND METHODS: Data on the total number and the ABO groups of plasma unit distributions were obtained from nine American blood collectors (ABCs) and nine national or provincial blood services (NPBS) from 2007 through 2016. Plasma distributions to trauma hospitals by five ABCs and four NPBS were also analyzed. RESULTS: The overall number of plasma unit distributions from ABCs decreased by 23.1% from 2007 to 2016, but the relative proportion of distributed AB plasma units increased during the same period. The NPBS (excluding the Japanese Red Cross [JRC]) also had a 35.4% decrease in the overall number of plasma unit distributions with an increase in the relative proportion of AB plasma distributions between 2007 and 2016. The JRC, however, reported an increase in the overall number of plasma distributions by 13.5% in 2016 compared to 2007. The proportion of low-titer A plasma distributions increased to 1.6% of total plasma distributions by ABCs in 2016. There was a trend of distributing increasing proportions of group AB plasma units to trauma hospitals over the 10-year period. CONCLUSION: Although the number of plasma unit distributions has decreased at many blood collectors over time, the proportion of AB units has increased at both ABCs and NPBS.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".