Performance characteristics of a novel blood bag in‐line closure device and subsequent product quality assessment
Bibliographic record
Abstract
BACKGROUND: In high-volume processing environments, manual breakage of in-line closures can result in repetitive strain injury (RSI). Furthermore, these closures may be incorrectly opened causing shear-induced hemolysis. To overcome the variability of in-line closure use and minimize RSI, Fresenius Kabi developed a new in-line closure, the CompoFlow, with mechanical openers. STUDY DESIGN AND METHODS: The consistency of the performance of the CompoFlow closure device was assessed, as was its effect on component quality. A total of 188 RBC units using CompoFlow blood bag systems and 43 using the standard bag systems were produced using the buffy coat manufacturing method. Twenty-six CompoFlow platelet (PLT) concentrates and 10 control concentrates were prepared from pools of four buffy coats. RBCs were assessed on Days 1, 21, and 42 for cellular variables and hemolysis. PLTs were assessed on Days 1, 3, and 7 for morphology, CD62P expression, glucose, lactate, and pH. A total of 308 closures were excised after processing and the apertures were measured using digital image analysis. RESULTS: The use of the CompoFlow device significantly improved the mean extraction time with 0.46±0.11sec/mL for the CompoFlow units and 0.52±0.13sec/mL for the control units. The CompoFlow closures showed a highly reproducible aperture after opening (coefficient of variation, 15%) and the device always remained opened. PLT and RBC products showed acceptable storage variables with no differences between CompoFlow and control. CONCLUSIONS: The CompoFlow closure devices improved the level of process control and processing time of blood component production with no negative effects on product quality.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".