An overview and comparison of haemovigilance reporting forms across six countries relative to the WHO template
Bibliographic record
Abstract
OBJECTIVES: Haemovigilance monitors, identifies, reports, investigates, and analyses adverse event near-misses and reactions related to transfusion and blood product manufacture. METHODS: This was an observational study in which we analyzed and compared the WHO haemovigilance template form for hospital and blood establishment haemovigilance reporting forms from Australia, Canada, India, New Zealand, South Africa, and the United States. All data from these reporting systems/forms was tabulated. The study analyzed data elements from each form, scoring them based on their presence in the WHO template and country forms. Higher scores indicated greater comparability and more comprehensive data collection. RESULTS: We identified 57 data fields in haemovigilance reporting forms from six countries and the WHO template, essential for collecting information on suspected transfusion products and reactions. The US FDA form has the most fields at 40 (70 %), followed by Canada with 33 (58 %) and India with 27 (47 %). New Zealand's form has the fewest at 16 (28 %), followed by South Africa with 17 (30 %). CONCLUSIONS: Effective haemovigilance systems require time and commitment to develop, often starting small and growing with stakeholder involvement. A straightforward reporting form, accessible to all, is crucial for success.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".