Methodological considerations for linked blood donor‐component‐recipient analyses in transfusion medicine research
Bibliographic record
Abstract
In recent years, there has been a concerted effort to improve our understanding of the quality and effectiveness of transfused blood components. The expanding use of large datasets built from electronic health records allows the investigation of potential benefits or adverse outcomes associated with transfusion therapy. Together with data collected on blood donors and components, these datasets permit an evaluation of associations between donor or blood component factors and transfusion recipient outcomes. Large linked donor-component recipient datasets provide the power to study exposures relevant to transfusion efficacy and safety, many of which would not otherwise be amenable to study for practical or sample size reasons. Analyses of these large blood banking-transfusion medicine datasets allow for characterization of the populations under study and provide an evidence base for future clinical studies. Knowledge generated from linked analyses have the potential to change the way donors are selected and how components are processed, stored and allocated. However, unrecognized confounding and biased statistical methods continue to be limitations in the study of transfusion exposures and patient outcomes. Results of observational studies of blood donor demographics, storage age, and transfusion practice have been conflicting. This review will summarize statistical and methodological challenges in the analysis of linked blood donor, component, and transfusion recipient outcomes.
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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 | 0.735 | 0.826 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.008 | 0.016 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.010 | 0.010 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".