An international survey on the role of the hospital transfusion committee
Bibliographic record
Abstract
BACKGROUND: Hospital transfusion committees (HTCs) can oversee all aspects of transfusion practice at a hospital. This survey sought to identify which quality variables were being reported at HTCs around the world. STUDY DESIGN AND METHODS: A working party composed of members of the Biomedical Excellence for Safer Transfusion (BEST) collaborative developed a survey of quality variables that could be potentially presented at HTC meetings. The survey was electronically sent to all BEST members who were encouraged to complete it if they were active on an HTC and to send it to other colleagues with similar experience. An expert panel was convened to determine which quality variables are the most important for review at HTC meetings. RESULTS: There were 121 respondents; the majority were from Europe (52%), Asia (19%), or North America (19%). Most respondents (68%) were at university hospitals. Of the 117 (97%) respondents with an HTC, the committee most often met quarterly (42%) and reviewed transfusion reactions (79%) and risk management-reported events (52%). The HTCs most commonly included transfusion medicine physicians, anesthesiologists, and other physicians who regularly transfuse blood products. Some of the most commonly reported quality variables included number of blood products transfused, wasted, and expired and the number of improperly labeled specimens. The expert panel analysis revealed that some variables that were deemed important were not being frequently reported at HTCs. CONCLUSION: There is variability in the variables being reported at HTCs around the world with some important variables not frequently reported.
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".