Clinical Outcomes Following Institution of the Canadian Universal Leukoreduction Program for Red Blood Cell Transfusions
Bibliographic record
Abstract
CONTEXT: A number of countries have implemented a policy of universal leukoreduction of their blood supply, but the potential role of leukoreduction in decreasing postoperative mortality and infection is unclear. OBJECTIVE: To evaluate clinical outcomes following adoption of a national universal prestorage leukoreduction program for blood transfusions. DESIGN, SETTING, AND POPULATION: Retrospective before-and-after cohort study conducted from August 1998 to August 2000 in 23 academic and community hospitals throughout Canada, enrolling 14 786 patients who received red blood cell transfusions following cardiac surgery or repair of hip fracture, or who required intensive care following a surgical intervention or multiple trauma. INTERVENTION: Universal prestorage leukoreduction program introduced by 2 Canadian blood agencies. A total of 6982 patients were enrolled during the control period and 7804 patients were enrolled following prestorage leukoreduction. MAIN OUTCOME MEASURES: All-cause in-hospital mortality and serious nosocomial infections (pneumonia, bacteremia, septic shock, all surgical site infections) occurring after first transfusion and at least 2 days after index procedure or intensive care unit admission. Secondary outcomes included rates of posttransfusion fever and antibiotic use. RESULTS: Unadjusted in-hospital mortality rates were significantly lower following the introduction of leukoreduction compared with the control period (6.19% vs 7.03%, respectively; P =.04). Compared with the control period, the adjusted odds of death following leukoreduction were reduced (odds ratio [OR], 0.87; 95% confidence interval [CI], 0.75-0.99), but serious nosocomial infections did not decrease (adjusted OR, 0.97; 95% CI, 0.87-1.09). The frequency of posttransfusion fevers decreased significantly following leukoreduction (adjusted OR, 0.86; 95% CI, 0.79-0.94), as did antibiotic use (adjusted OR, 0.90; 95% CI, 0.82-0.99). CONCLUSION: A national universal leukoreduction program is potentially associated with decreased mortality as well as decreased fever episodes and antibiotic use after red blood cell transfusion in high-risk patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".