Bibliographic record
Abstract
To the Editor—We would like to update our experience in reducing the risk of mistransfusion.1 In our earlier report,2 we collected data on mislabeled blood bank samples, classified by severity and by location. Overall, the incidence of mislabeling was 0.5%; about one-third of these events involved miscollected specimens discovered when an ABO/Rh result on a current specimen did not match the historical blood type on file, also known as “wrong blood in tube” (WBIT) samples. Targeting patient care areas contributing to recurring WBITs with timely feedback led to a decrease in the number of WBIT specimens. Subsequent to the initial effort, we implemented a 2-signature requirement for all blood bank samples. The number of WBIT cases has decreased from an average of 5.8 per quarter to 2.3 per quarter (see Figure). Although our ultimate goal is to use electronic bar code specimen labeling at the bedside, which has been demonstrated to reduce misidentification of clinical laboratory samples,3 followed by an electronic blood unit verification system,4 we are gratified that a “low-tech” strategy, in conjunction with continued education among laboratory and nonlaboratory phlebotomists, can reduce the risk of mistransfusion. We plan to trend our future efforts using statistical process control tools.5
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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 | 0.008 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.010 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".