Implementation of a blood bank generated tube for second blood group determination: Challenges, yield, and cost
Bibliographic record
Abstract
BACKGROUND: The second blood group determination or group check sample is a process of verifying the ABO group with a second blood sample prior to transfusion. It has been used to detect errors related to wrong blood in tube (WBIT) events and reduce the risk of ABO-incompatible transfusions. To prevent the clinical team from collecting the group check sample at the same time as the first sample, a tan top tube only available from the blood bank was introduced for second blood group determinations if drawn within 24 h of the first group and screen. STUDY DESIGN AND METHODS: This is a retrospective study analyzing data from 2005 to 2020 before and after the implementation of the blood bank supplied tan top tube for group check. The number of WBIT events, transfusion delays, and health care costs were determined. RESULTS: The number of WBIT events remained unchanged throughout the time period. No delays in transfusion or procedure were reported due to the tan top tube group check. There was no increase in group O transfusions over time. In comparison to using an ethylenediaminetetraacetic acid (EDTA) tube, the tan top tube was estimated to add an extra yearly cost of $790.79 Canadian dollars. CONCLUSION: Second blood group determination using the blood bank supplied tan top tube did not increase the number of WBIT events detected but ensured an independent sample draw. A minimal incremental cost of implementing the tan top tube was noted with no delay in transfusions or procedures.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.000 |
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".