Automated collection of blood components: their storage and transfusion
Bibliographic record
Abstract
The performance of an automated device, the Gambro TRIMA, was evaluated for component production, and an in vivo assessment of the platelets was carried out. Red cell concentrates (RCCs), platelets and plasma were collected and stored according to standard blood bank procedures and evaluated for quality by in vitro measurements. Additionally, single-donor platelets (n=10) were transfused to thrombocytopenic patients after 5 days of storage. Platelet counts were measured after 1 h and the corrected count increment (CCI) was calculated. No significant changes were seen before or after procedure in donor haemoglobin, haematocrit, coagulation factors or platelet count. Return-line samples showed no increase in the level of plasma haemoglobin. Plasma haemoglobin and potassium increased following RBC storage, but there was no change in the red cell number. Platelet aggregation decreased from 52 to 11% (adenosine diphosphate) and the Kunicki morphology score dropped from 379 to 174. Little change was seen in the hypotonic shock response (69-63%) or in the percentage of CD62 expression (4.8-19.8) over time. The CCI averaged 28+/-26 x 10(3) microL(-1) in 10 patients 1 h after transfusion. The TRIMA machine collects RCCs, platelets and plasma in a variety of combinations in one session. For autologous collection, two units of RCC plus platelets can be collected at one time, reducing administrative and testing costs. The platelets have good in vivo recovery, as shown by the CCI values. An added advantage is that the TRIMA machine can be used in hospitals to generate components in times of shortage without the need for a component's laboratory.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".