Cryoprecipitate use in 25 Canadian hospitals: commonly used outside of the published guidelines
Bibliographic record
Abstract
BACKGROUND: Canadian Blood Services' disposition reports suggested considerable variation in cryoprecipitate use and prompted this national audit. STUDY DESIGN AND METHODS: Thirty-one institutions were invited to participate in a 2-month audit. Patient information and relevant laboratory and transfusion data were collected. Cryoprecipitate transfusions were categorized as appropriate if a fibrinogen level (taken 6 hr before/after transfusion) was not more than 1.0 g per L and inappropriate if the pretransfusion fibrinogen level was more than 1.0 g per L and posttransfusion fibrinogen level was more than 1.0 g per L or not performed. Appropriateness was categorized as undetermined if the pretransfusion fibrinogen level was not performed and the posttransfusion fibrinogen level was more than 1.0 g per L or not performed. RESULTS: Overall, 25 of 31 invited hospitals agreed to participate. A total of 4370 units of cryoprecipitate were transfused in 603 events to 453 patients representing 62 percent of cryoprecipitate issued to hospitals during the time period. Comparison of the number of units of cryoprecipitate per 100 units of red blood cells (RBCs) transfused by each institution showed significant variation in practice (mean, 9 per 100 RBCs; range, 2 to 27 units). The single most common indication for cryoprecipitate was cardiac surgery (45.4% of events). Overall, 24 percent of cryoprecipitate transfusions were considered to be appropriate (pretransfusion fibrinogen level <or=1 g/L in 19% and posttransfusion fibrinogen level
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".