A survey of the demographics of blood use
Bibliographic record
Abstract
Epidemiological information was obtained by a series of questions to experts in the field of epidemiology of transfusion from the United States, England, Australia and Denmark. Although it became clear that the methods for collecting the data had differed between the countries, useful information was obtained for all questions. The data highlighted some major differences between the countries: the incident rate for red cell transfusion varied from 44.7 to 54.1 units, for platelets from 2.0 to 6.0 units and for plasma from 4.8 to 13.8 units transfused per 1000 population per year. Age and sex distribution of transfused patients was similar in all countries. Most of the red cell products are transfused to older recipients, and the distribution between men and women is approximately equal. The distribution for platelets is over a wider age range, and the difference between men and women is marked, with men predominating in all countries. The distribution for plasma is also directed to the elderly, and there is a predominance of men. The relationship between the disease or surgical procedure and the use of blood products was similar between countries. The use of red cells in cardiovascular surgery predominated. Neoplasms and digestive disorders were also prevalent. Neoplasms, including those relating to haematology, were the main use for platelets, but cardiovascular surgery was also important. In all countries, plasma is largely used in cardiovascular surgery. Two countries provided data relating to the number of units per transfusion episode including information relating to massive transfusion. In Australia, red cell use of >or=50 units per episode was largely associated with multiple traumas. In Denmark, it was associated with gastrointestinal bleeding and various medical requests.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".