Effect of blood donor characteristics on transfusion outcomes: a protocol for systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Optimal selection of blood donors is of paramount importance in ensuring the safety of blood products. The current selection process is concerned principally with the safety of the blood donor and the safety of the patient that receives the blood. Recent evidence suggests that the characteristics of the donor may affect transfusion outcomes for the recipient. METHODS: We will conduct a systematic review of the association between major blood donor characteristics and red blood cell (RBC) transfusion outcomes. The primary objective is to assess the association of blood donor characteristics and the risk of adverse short-term and long-term clinical outcomes after RBC transfusion. We will search MEDLINE, EMBASE, Cochrane Central databases, as well as perform manual searches of top transfusion medical journals for prospective and retrospective studies. Study characteristics will be reported and the methodological quality of studies will be assessed. When appropriate, we will provide pooled odds ratio with 95% confidence intervals of the effect estimates, study clinical heterogeneity using pre-defined sensitivity and subgroup analyses, and study statistical heterogeneity using the I2 test. DISCUSSION: The results of this systematic review will provide an evidence base regarding the potential clinical effects of donor characteristics on transfusion recipients to better guide policy and clinical practice. The evidence gathered from this review will also identify strengths and weaknesses of published studies regarding donor characteristics and transfusion outcomes and will identify knowledge gaps to inform future research in this field of transfusion medicine. TRIAL REGISTRATION: PROSPERO Registration Number: CRD42013006726.
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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 | 0.079 | 0.127 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.021 | 0.026 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.056 | 0.005 |
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".