Evaluation of a Pre-Filled Table and a Flowchart-Based Algorithm as Cognitive Aids to Reduce Deviations in Dose Calculation for Intraoperative Red Blood Cell Transfusions in Children—An International Web-Based Simulation
Bibliographic record
Abstract
Background: Transfusion of red blood cell concentrate can be life-saving, but requires accurate dose calculations in children. Aims: We tested the hypothesis that cognitive aids would improve identification of the correct recommended volumes and products, according to the German National Transfusion guidelines, in pediatric transfusion scenarios. Methods: Four online questionnaire-based scenarios, two with hemodynamically stable and two with hemodynamically unstable children, were sent to German and international pediatric anesthetists for completion. In the two stable scenarios, participants were given pre-filled tables that contained all required information. For the two emergency scenarios, existing algorithms were used and required calculation by the user. The results were classified into three categories of deviations from the recommended values (DRV): DRV120 (<80% or >120%), as the acceptable variation; DRV 300 (<33% or >300%), the deviation of concern for potential harm; and DRV 1000 (<10% or >1000%), the excessive deviation with a high probability of harm. Results: A total of 1.458 pediatric anesthetists accessed this simulation questionnaire, and 402 completed questionnaires were available for analysis. A pre-filled tabular aid, avoiding calculations, led to a reduction in deviation rates in the category of DRV120 by 60% for each and of DRV300 by 17% and 20%, respectively. The use of algorithms as aids for unstable emergencies led to a reduction in the deviation rate only for DRV120 (20% and 15% respectively). In contrast, the deviation rates for DRV300 and DRV1000 rose by 37% and 16%, respectively. Participants used higher transfusion thresholds for the emergency case of a 2-year-old compromised child than for the stable case with a patient of the same age (on average, 8.6 g/dL, 95% CI 8.5–8.8 versus 7.1 g/dL, 95% CI 7.0–7.2, p < 0.001) if not supported by our aids. Participants also used a higher transfusion threshold for unstable children aged 3 months than for stable children of the same age (on average, 8.9 g/dL, 95% CI 8.7–9.0 versus 7.9 g/dL, 95% CI 7.7–8.0, p < 0.001). Conclusions: The use of cognitive aids with precalculated transfusion volumes for determining transfusion doses in children may lead to improved adherence to published recommendations, and could potentially reduce dosing deviations outside those recommended by the German national transfusion guidelines.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".