Hospital‐based transfusion error tracking from 2005 to 2010: identifying the key errors threatening patient transfusion safety
Bibliographic record
Abstract
BACKGROUND: This report provides a comprehensive analysis of transfusion errors occurring at a large teaching hospital and aims to determine key errors that are threatening transfusion safety, despite implementation of safety measures. STUDY DESIGN AND METHODS: Errors were prospectively identified from 2005 to 2010. Error data were coded on a secure online database called the Transfusion Error Surveillance System. Errors were defined as any deviation from established standard operating procedures. Errors were identified by clinical and laboratory staff. Denominator data for volume of activity were used to calculate rates. RESULTS: A total of 15,134 errors were reported with a median number of 215 errors per month (range, 85-334). Overall, 9083 (60%) errors occurred on the transfusion service and 6051 (40%) on the clinical services. In total, 23 errors resulted in patient harm: 21 of these errors occurred on the clinical services and two in the transfusion service. Of the 23 harm events, 21 involved inappropriate use of blood. Errors with no harm were 657 times more common than events that caused harm. The most common high-severity clinical errors were sample labeling (37.5%) and inappropriate ordering of blood (28.8%). The most common high-severity error in the transfusion service was sample accepted despite not meeting acceptance criteria (18.3%). The cost of product and component loss due to errors was $593,337. CONCLUSION: Errors occurred at every point in the transfusion process, with the greatest potential risk of patient harm resulting from inappropriate ordering of blood products and errors in sample labeling.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".