Reporting of transfusion reactions in a hospital in Brazil.
Bibliographic record
Abstract
Dear Sir, Recently Maria de Fatima Alves Fernandes and her colleagues reported some results and perspectives about haemovigilance in Brazil1, so here we want to present our haemovigilance findings in a regional hospital of the state of Rio Grande do Sul, Brazil. Haemovigilance systems started to be established in 1992 in Japan2 and then in France (1994)3, Germany (1994), Greece (1995), Luxembourg and the United Kingdom (1996)4. The principle aims of haemovigilance programmes are to detect and analyse untoward effects of blood transfusion in order to correct their causes and to prevent recurrence and, thereby, safeguard patients' safety. There are differences in reporting between haemovigilance systems. United Kingdom and Ireland operate with voluntary reporting of serious and incompatible blood component transfusion reactions while in the Netherlands and Quebec all reactions are reported on a voluntary basis. In France the system involves mandatory reporting of all reactions5. A national haemovigilance system was officially implemented in Brazil in 2002 starting with a network of 100 tertiary hospitals with voluntary reporting of all reactions. In 2009 the reported rate of reactions was 0.91/1,000 transfusions1. Information on reporting of transfusion reactions in non-tertiary hospitals outside this network in Brazil has not been published so far. Our aim is to present the reporting of transfusion reactions in a regional hospital of the Southern state of Rio Grande do Sul, Brazil. During a 5-year period (2006–2010) 28 reactions were reported in 10,790 transfusions (2.6/1,000). The transfusion reactions were identified by nurses who first contacted the physician to decide how to manage the reaction and then subsequently made a written report to the blood bank, notifying the name of the patient, the characteristics of the reaction and its severity. The majority of the transfused patients had chronic benign diseases, cancer, previous accidents or were waiting for or recovering from surgery. Later the reported reactions were classified as febrile, allergic, haemolytic and both febrile and allergic. No severe reaction was observed. The reactions were of a febrile type in 17 cases, allergic in seven cases, haemolytic in one case and had both febrile and allergic features in one other case; in two reports no information was given about the type of reaction. The observed reporting of reactions (2.6/1,000 transfused units) did not differ much from that in France (2.83/1,000 units) and the Netherlands (2.9/1,000 units)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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".