The effects of pen ink and surface disinfectants on red blood cells stored in plasticized polyvinylchloride transfusion bags
Bibliographic record
Abstract
BACKGROUND: Each unit of red blood cells (RBCs) produced represents a significant cost to the healthcare system. Unnecessary blood wastage should be minimized. In clinical settings, alterations to blood component bags after issue from the protected setting of the blood bank include pen markings, and those that are exposed to an infectious environment require surface disinfecting. These units may be discarded due to unclear effects on RBC quality. In this study, we investigate whether pen markings or surface disinfection negatively affects the quality of packed RBCs and whether pen ink diffuses through the blood bag. STUDY DESIGN AND METHODS: RBC bags were marked with pens (water, oil, or alcohol-based) or subjected to surface disinfection (ethanol, hydrogen peroxide [Preempt wipes], or benzalkonium chloride-based wipes [CaviWipes]) and sampled 24 h after applying the treatment and at day 42 post collection (n = 3 for each condition). The samples were analyzed for RBC in vitro quality markers. The presence of any ink in the RBC bags was investigated using mass spectrometry (n = 2). RESULTS: Data from 24 h and day 42 time points indicated no differences in RBC count, mean corpuscular volume, morphology, deformability, potassium content, or hemolysis for either pen markings or disinfectants when compared with their untreated controls (p > .05). No trace of ink was detected inside the bag. CONCLUSION: RBC units marked with ballpoint, gel, or Sharpie pens do not suffer a loss of in vitro quality, nor do RBC units which have been surface disinfected with 70% ethanol, Preempt wipes or CaviWipes.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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 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".