Changing trends in blood transfusion: an analysis of 244,013 hospitalizations
Bibliographic record
Abstract
BACKGROUND: Identifying recipients of blood transfusion and the trends in transfusion are needed to properly identify and target clinical services in need of patient blood management strategies. We determined the proportion of admissions to each clinical service that received blood, the mean number of units utilized, and the 5-year trends in utilization. STUDY DESIGN AND METHODS: We used a large administrative database, a repository for three campuses of one university-affiliated hospital, and included all adults that were hospitalized from November 1, 2006, to June 2012. The data were analyzed as the proportion of admissions transfused and the mean number units transfused per admission. RESULTS: Of 244,013 hospitalizations, 38,265 received at least one transfusion (29,165 for red blood cells [RBCs], 6760 for plasma, and 5795 for platelets [PLTs]). Although there has been a gradual decrease in the mean number of RBCs transfused (percent change, -9.8%; p = 0.002), an increase in the proportion of admissions receiving RBCs (17.2% increase, p < 0.0001) and PLTs (31.5% increase, p < 0.0001) was apparent while there has been a decrease in the proportion of admissions receiving plasma (23.9% decrease, p < 0.0001). Eight percent of cardiology admissions received RBCs, and the highest mean RBC utilization per admission, aside from the stem cell transplantation service, occurred in cardiology and critical care hospitalizations (mean, 4.7 units/hospitalization). CONCLUSION: Although there has been a reduction in the mean RBC units used, there has been an increase in the proportion of hospitalizations transfused. A better understanding of the indications for transfusion is required to facilitate the development of targeted blood conservation strategies.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".