Impact of blood volume, air exposure duration, transport duration, and testing delay on plasma total carbon dioxide in simulated open collections using microtainers
Bibliographic record
Abstract
Exposing blood specimens to air reduces plasma total carbon dioxide (TCO 2 ). We evaluated the degree of TCO 2 reduction attributed to open collection of neonatal blood in BD microtainers® (microtainers), microtainer transport duration and delayed testing of open plasma aliquots. Venous blood was aliquoted into open microtainers in a 3x4 factorial design to simulate combined effects of blood volume (0.2–0.6 mL) and air exposure duration (0–5 min), with blood drawn in vacutainers as a control. Separate effects of in-hospital transport duration (0–120 min; whole blood), off-site transport duration (0–24 h; centrifuged whole blood), and the duration plasma aliquots remained open (0–120 min) were evaluated by repeated testing. Findings were analyzed using repeated-measures ANOVA and Student’s T-tests. In the factorial experiment, mean plasma TCO 2 in microtainers was on average 3.5 mmol/L lower than in vacutainers. Smaller blood volume but not greater air exposure duration significantly (p < 0.05) reduced TCO 2 . Mean TCO 2 in filled (0.6 mL; 1–5 min air exposure) microtainers was on average 2.9 mmol/L lower than in vacutainers. Simulated off-site transport of microtainers containing centrifuged whole blood significantly reduced TCO 2 (4 h; mean change = -1.5 mmol/L), as did delayed testing of aliquoted plasma (15 min; mean change = -1.3 mmol/L). Plasma TCO 2 decreased with reduced microtainer blood volume, extended off-site transport duration of centrifuged whole blood and testing delay of aliquoted plasma. To minimize TCO 2 reduction, microtainers should be fully filled and tested rapidly. Laboratories should also consider whether an interpretive comment, correction factor or separate reference intervals are appropriate for these tests.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".