A summary of the Transfusion Error Surveillance System: 2008 - 2011
Bibliographic record
Abstract
BACKGROUND: Although Canada has one of the safest blood systems in the world, transfusion errors can occur at any time from the moment of collection through to the transfusion of blood and blood products. The Transfusion Error Surveillance System (TESS) was initiated by the Public Health Agency of Canada (the Agency) to monitor transfusion-related errors occurring at any point in the transfusion chain. OBJECTIVE: To offer an analysis of the TESS data reported from 2008 to 2011. METHODS: Between 2008 and 2011, 12 to 15 hospitals from four provinces participated in the TESS. Reports on all transfusion-related errors were sent electronically on a quarterly basis to the Agency where they were consolidated, cleaned, validated and analyzed. Different types of transfusion errors were categorized by time of discovery (pre- or post-transfusion) and their potential impact on the patient's health. The occurrence rates of different types of errors were calculated using corresponding denominator data. Results were grouped by the transfusion capacity of reporting hospitals. RESULTS: Between 2008 and 2011, a total of 34,088 transfusion-related errors were reported. Of these, 33,622 (98.6%) were detected prior to transfusion. The most commonly reported were errors related to the collection (40.1%) and handling (10.4%) of blood samples. Of the remaining 466 (1.4%) that were detected after transfusion, 66 were of high potential severity and 16 of them resulted in adverse reactions in recipients. Inappropriate / incorrect / no product order accounted for over 56% (n=9) of these errors and the most common adverse reaction was transfusion-associated circulatory overload which occurred in eight (50%) of the patients that developed adverse reactions. CONCLUSION: The TESS data from 2008 to 2011 demonstrates that blood transfusions are both safe and efficient in Canadian hospitals participating in the surveillance and also highlights the most common and most harmful errors that may be targeted for corrective actions.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".