Switching donor cells as a major source of error in compatibility testing
Bibliographic record
Abstract
The most likely cause of fatality in blood transfusion is transfusion of the wrong unit of blood to a patient. This type of error is usually attributed to improper patient identification at the time of sample collection or transfusion. A retrospective analysis of the results of an external proficiency testing program identified a common source of error occurring during laboratory testing that has not been previously reported. Results were analyzed when major errors were assigned to laboratories for obviously switching donor units in compatibility testing and/or subsequent investigation. In 24 surveys sent to extended testing (Level A) laboratories and 18 sent to basic testing (Level B) laboratories, the antigenic composition of the two donor cells made it possible to determine whether the cells had been switched. Seven errors were assigned to Level A participants for switching donor units during testing, constituting 38.9 percent of the 18 major errors assessed. Level B participants were assigned eight errors for switching donor units, 26.7 percent of the 30 major errors assessed. Approximately one-third (31.3 percent) of major errors committed on 42 proficiency testing surveys were caused by switching of donor cells during compatibility testing. This type of error may result in transfusion of an incompatible donor unit.
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 | 0.016 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".