Can Policy Decisions in Transfusion Medicine Be Evidence‐Based?*
Bibliographic record
Abstract
SUMMARy If the concept of evidence‐based medicine were considered at a policy level, it would likely dictate that policy decisions be made on the basis of the best available research evidence. In transfusion medicine, however, decisions are based on a broader range of inputs, and the criteria for evaluating the efficacy and/or cost‐effectiveness of proposed interventions differ from those used in other areas. Reasons why policy decisions in transfusion medicine are often based on considerations other than solely the best available research evidence include public perceptions of transfusion as being an inherently “unsafe” intervention, public expectations with regard to transfusion safety, and proposals for applying the precautionary principle to transfusion medicine. In the authors' opinion, use of the precautionary principle may be justified in addressing transfusion risks that are viewed as “dread events,” but this principle should not be applied indiscriminately to all transfusion risks.
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 | 0.182 | 0.513 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.025 | 0.021 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.038 | 0.025 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".