Risk‐based decision making in transfusion medicine
Bibliographic record
Abstract
Formal processes to assess risk are well established in numerous areas of society including the environment, transportation, energy and food production sectors as well as some areas of health care such as new drugs or other therapeutic goods. However, these processes and their associated frameworks have only recently come to be used to make decisions in blood transfusion practice or in blood system policy development. This review describes the evolution of the use of risk-based decision making and discusses the elements that should be considered in its application to blood system issues. Following the identification and characterization of the risk, a structured process is undertaken to assess the magnitude of the risk and the level of risk reduction that can reasonably be achieved in the context of the complexity of the risk management action proposed and its cost. Inputs must be sought from appropriate subject matter experts, but also from those who can consider issues of ethics and social values. Engagement of the public is an essential step. Proposed interventions should be assessed for their likelihood of mitigating the risk and the proportional resource allocation in comparison with similar risks to the blood system or health system. Examples are provided of how a risk-based decision-making framework is used to address identified risks in the blood system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".