A retrospective study evaluating single‐unit red blood cell transfusions in reducing allogeneic blood exposure
Bibliographic record
Abstract
Although guidelines recommend the use of single-unit red blood cell (RBC) transfusions to minimize allogeneic blood exposure, clinical practice remains dominated by two-unit transfusions. This study assesses the potential impact of a single-unit transfusion policy on reducing RBC utilization. We performed a retrospective analysis of adult patients admitted to a tertiary care hospital who received one or two RBC units. In subjects transfused two units, the effect of one unit was estimated by dividing the change in haemoglobin by 2. The proportion of patients reaching a haemoglobin threshold of 70, 75, 80, 85 and 90 g L(-1) with a single RBC unit was estimated. Of 302 included patients, only 65 received a one-unit transfusion. Based on thresholds of > or = 90, > or = 80 and > or = 70 g L(-1), a single-unit transfusion would be sufficient in 42.0% (RRR = 0.54), 79.6% (RRR = 0.23) and 98.0% (RRR = 0.02) of cases, respectively. This corresponds to 0.21, 0.57 and 0.82 mean RBC units saved per patient. In the orthopaedic subpopulation, the mean RBC units saved are 0.53, 0.88 and 1.00 for the same haemoglobin targets. Adopting a policy of transfusing RBC in single-unit aliquots could significantly improve RBC utilization and decrease patient exposure to allogeneic blood.
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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".