Survey of cryoprecipitate production and cryoprecipitate and fibrinogen concentrate utilization in North America: Variable practices observed from July 2016 to June 2021, The <scp>BEST</scp> Collaborative Study
Bibliographic record
Abstract
BACKGROUND: Cryoprecipitated antihemophiliac factor (cryo) is primarily used to replenish fibrinogen in acquired coagulopathy. Little has been reported about its usage patterns within hospitals with respect to patient population, frequency of use, and dosing. STUDY DESIGN AND METHODS: Cryo production and usage data were collected over 5 years from July 1, 2016, to June 30, 2021, from nationally based blood collection centers in the United States (U.S. n = 2) and Canada (n = 2) and from eight large academic hospitals, respectively. Usage data for a similar product, purified fibrinogen concentrate were also collected from four hospitals. RESULTS: Two U.S. blood collectors reported increases in cryo production normalized to total whole blood collections from 17.4% in 2016 to 22.3% in 2021 and from 16.4% in 2016 to 20.2% in 2021. In contrast, in Canada cryo manufacturing increased slightly in one region (11.3%-12.9%) and decreased (8.0%-2.0%) in the other. Cryo utilization, defined as numbers of patients treated normalized to the inpatient census, and dose administered per patient did not consistently increase and differed significantly between hospitals participating in the study (p < .0001). Likewise, the departments that most frequently transfused cryo varied between hospitals. Similarly, variations in practice were observed for fibrinogen concentrate usage. CONCLUSION: While much of Canada and Europe have moved towards using fibrinogen concentrate, two large U.S. collectors have increased cryo distribution. The lack of standardization and variability in the clinical practice regarding the use of cryo and fibrinogen concentrate reported by study sites may be attributable to practitioner preference, availability, and/or cost rather than adherence to published evidence or guidelines.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".