Sample collection and sample handling errors submitted to the transfusion error surveillance system, 2006 to 2015
Bibliographic record
Abstract
BACKGROUND: In Canada, transfusion-related errors are voluntarily reported to a tracking system with the goal to systematically improve transfusion safety. This report provides an analysis of sample collection (SC) and sample handling (SH) errors from this national error-tracking system. STUDY DESIGN AND METHODS: Errors from 2006 to 2015 from 23 participating sites were extracted. A survey was conducted to obtain information regarding institutional policies. Samples received in the blood bank were used to calculate rates. "Wrong blood in tube" (WBIT) errors are blood taken from wrong patient and labeled with intended patient's information, or blood taken from intended patient but labeled with another patient's information. RESULTS: A total of 42,363 SC and 14,666 SH errors were reported. Predefined low-severity (low potential for harm) and high-severity errors (potential for fatal outcomes) increased from 2006 to 2015 (low SC, SH: 13-27, 3-12 per 1000; high SC, SH: 1.9-3.7, 0.5-2.0 per 1000). The WBIT rate decreased from 12 to 5.8 per 10,000 between 2006 and 2015 (p < 0.0001). The overall WBIT rate was 6.2 per 10,000, with variability by site (median, 0.3 per 10,000; range, 0-17 per 10,000). Sites with error detection mechanisms, such as regrouping second sample requirements, had lower error rates than sites that did not (SC, SH: 12, 1 per 1000 samples vs. 17, 3 per 1000 samples; p < 0.0001). CONCLUSION: WBIT rates decreased significantly. Low-severity error rates are climbing likely due to increased ascertainment and reporting. Prevention studies are necessary to inform changes to blood transfusion standards to eliminate these errors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".