Improved Sample Quality and Decreased Turnaround Time When Using Plasma Blood Collection Tubes with a Mechanical Separator in a Large University Hospital
Bibliographic record
Abstract
BACKGROUND: Serum is commonly used for clinical chemistry testing but many conditions can affect the clotting process, leading to poor sample quality and impaired workflow. With serum gel tubes, we found a high proportion of sample probe aspiration errors on our Beckman AU5800 analyzers. We decided to implement the BD Barricor™ plasma tubes, and we validated an off-specification centrifugation scheme and verified that results obtained for 65 chemistry and immunochemistry tests were comparable to those obtained in serum gel tubes. Finally, we evaluated the impact of this new tube on sample error rate and laboratory turnaround time. METHODS: To validate centrifugation settings, 50 paired samples were collected in Barricor tubes and centrifuged at 1912 × g for 10 min or 5 min (off-specification). To compare serum gel tubes with Barricor plasma tubes, 119 paired samples were collected from volunteers and results were analyzed using weighed Deming regression. Finally, the proportion of aspiration errors and laboratory TAT for potassium were measured before and after implementing Barricor tubes. RESULTS: Barricor tubes showed clinically acceptable equivalence to serum gel tubes for the studied analytes, and the off-specification centrifugation scheme did not affect the results. Implementing Barricor tubes improved the laboratory workflow by decreasing the aspiration error rates (2.01% to 0.77%, P < 0.001) and lowering hemolysis (P < 0.001). The laboratory TAT for potassium were also significantly lowered (P < 0.001). CONCLUSION: Use of Barricor tubes instead of serum gel tubes leads to better sample quality, shorter more reproducible laboratory TAT, and decreases costs associated with error management.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".