Plasma transfusion practice: A five‐year audit of plasma transfusion at 23 hospitals
Bibliographic record
Abstract
BACKGROUND: There is limited high-quality evidence to guide plasma transfusion, and plasma transfusion practices remain variable. STUDY DESIGN AND METHODS: This is a retrospective cohort study that included adult medical and intensive care unit (ICU) inpatients (age ≥ 18 years) admitted to 23 hospitals in Canada between January 1, 2017, and December 31, 2022, when both whole blood derived (~290 mL) and apheresis plasma (~250 mL) were available for transfusion. Nine additional hospital sites with missing plasma data or coagulation testing were excluded. Data collected included patient demographics, most responsible diagnoses, procedures, laboratory tests, transfusion information, and physician characteristics. RESULTS: Among 950,740 included hospital admissions at 23 hospitals, there were 11,163 admissions with plasma use, with 46,377 plasma units transfused. Of the plasma recipients, 63.5% were male, with a mean age of 61.2 years (SD 16.4). Most plasma transfusions were administered in the ICU (64.1%). The number of plasma units transfused per 1000 inpatient days across centers varied from a median (IQR) of 0.2 (0.1-0.4) to 11.0 (10.6-12.0) units. There was significant variability in pre-transfusion INR values across hospitals (ranging from a median (IQR) of 1.5 (1.3-1.9) to 2.5 (1.4-4.6)) and physician specialties (ranging from a median (IQR) of 1.4 (1.3-1.8) to 2.2 (1.8-3.0)). There was no significant change in plasma utilization over the study period. DISCUSSION: This study demonstrated variability in plasma utilization and pre-transfusion INR thresholds across hospitals and physician specialties. This highlights the importance of developing evidence-based guidelines and effective knowledge translation to guide appropriate plasma use.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".