Blood Microsampling for Complete Blood Count: Take Heed of Preanalytical Errors
Bibliographic record
Abstract
Background: The credibility of the result of a complete blood count is closely connected with the preanalytical phase. Objectives: This study evaluated accordance of filling of microtubes with manufacturer’s recommendation and assessed the effect of storage of overfilled and underfilled samples on the results of complete blood count. Design and Methods: Volume of blood samples collected into microtubes in the wards of the University Children's Hospital in Cracow during one month was analyzed. In the stability studies, overfilled and underfilled samples stored at ambient temperature were analyzed at 1, 2, 3 and 12 hours after phlebotomy. The analysis was made using the SYSMEX XT-1800i analyzer. Results: More than half of the analyzed samples were incorrectly filled. 63% of the samples were filled above the manufacturer's recommended volume and 15% of test-tubes were filled below the recommendation. We observed differences between collected blood volume in accordance to the age of patients (p=0.001). The storage of overfilled and underfilled microtubes for complete blood count for 1,2,3 and 12 h at room temperature had no effect on the results of this test. Conclusion: Medical staff does not follow the instructions of the manufacturers. It might lead to a decrease of the quality and credibility of the results.
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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 | 0.031 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".