Evaluating variability in use of intravenous albumin in patients undergoing surgery for cancer
Bibliographic record
Abstract
Background: Despite numerous randomized controlled trials finding that albumin is not associated with improved patient outcomes, transfusion practice is highly variable. We examined the variability and impact of albumin transfusion on outcomes in cancer surgery. Methods: We included consecutive adults undergoing cancer surgery between 2018 and 2021 in Ontario, Canada. The primary exposure was the proportion of patients who received perioperative albumin. The secondary outcomes were hospital length of stay and the incidence of infection, anemia, venous thromboembolism, and mortality in albumin-treated versus non-albumin-treated patients in a case–control analysis. Results: Of 155 166 cancer surgeries (66.8% female patients, median age 62.9 yr), 2.5% received perioperative albumin. The cancer surgery types with the highest proportion of patients receiving albumin were hepato-pancreato-biliary (24.8%) and colorectal (18.6%). Of 104 facilities, 12.5% had nonrandom outliers for albumin use in at least 1 cancer type (p = 0.0004). Patient outcomes were different in case–control matched cohorts for colorectal and hepato-pancreato-biliary surgeries, including a higher rate of infection, venous thromboembolism, and mortality in patients treated with albumin (cases) than those who were not (controls). Conclusion: Albumin transfusion rates were highly variable among hospitals for the same cancer type. Quality improvement initiatives are warranted to curtail unnecessary albumin transfusions in the perioperative period.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".