Pitfalls of reasoning in hospital‐based transfusion medicine
Bibliographic record
Abstract
INTRODUCTION: Hospital-based transfusion involves hundreds of daily medical decisions. Medical decision-making under uncertainty is susceptible to cognitive biases which can lead to systematic errors of reasoning and suboptimal patient care. Here we review common cognitive biases that may be relevant for transfusion practice. MATERIALS AND METHODS: Biases were selected based on categorical diversity, evidence from healthcare contexts, and relevance for transfusion medicine. For each bias, we provide background psychology literature, representative clinical examples, considerations for transfusion medicine, and strategies for mitigation. RESULTS: We report seven cognitive biases relating to memory (availability heuristic, limited memory), interpretation (framing effects, anchoring bias), and incentives (search satisficing, sunk cost fallacy, feedback sanction). CONCLUSION: Pitfalls of reasoning due to cognitive biases are prominent in medical decision making and relevant for hospital transfusion medicine. An awareness of these phenomena might stimulate further research, encourage corrective measures, and motivate nudge-based interventions to improve transfusion practice.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 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".