Blood transfusion risks and alternative strategies in pediatric patients
Bibliographic record
Abstract
Although the safety of the blood supply has been greatly improved, there still remain both infectious and noninfectious risks to the patient. The incidence of noninfectious transfusion reactions is greater than that of infectious complications. Furthermore, the mortality associated with noninfectious risks is significantly higher. In fact, noninfectious risks account for 87-100% of fatal complications of transfusions. It is concerning to note that the majority of pediatric reports relate to human error such as overtransfusion and lack of knowledge of special requirements in the neonatal age group. The second most frequent category is acute transfusion reactions, majority of which are allergic in nature. It is estimated that the incidence of adverse outcome is 18:100,000 red blood cells issued for children aged less than 18 years and 37:100,000 for infants. The comparable adult incidence is 13:100,000. In order to decrease the risks associated with transfusion of blood products, various blood-conservation strategies can be utilized. Modalities such as acute normovolemic hemodilution, hypervolemic hemodilution, deliberate hypotension, antifibrinolytics, intraoperative blood salvage, and autologous blood donation are discussed and the pediatric literature is reviewed. A discussion of transfusion triggers, and algorithms as well as current research into alternatives to blood transfusions concludes this review.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".