Evaluating Variation In Red Blood Cell Transfusion for Patients undergoing Elective Gastrointestinal Cancer Surgery
Bibliographic record
Abstract
Understanding variation in transfusion practice among patients undergoing gastrointestinal cancer surgery will support efforts working towards responsible transfusion use. We performed a population-based study of patients who underwent elective gastrointestinal cancer resection between 2007 and 2019 to measure variation in transfusion use across surgeons and hospitals. Variation was explored using funnel plots and multilevel regression. Of 59,964 patients, 18.0% were transfused. Wide variation in transfusion use among surgeons and hospitals was observed. Patient characteristics explained 12.8% of the variation. After adjusting for patient case-mix, between-surgeon and between-hospital differences were responsible for 2.8% and 2.1% of the variation, respectively, translating to an approximately 30% difference in the odds of transfusion for two similar patients treated by distinct surgeons or hospitals, respectively. Although transfusion provision depends on patient factors, important variation across surgeons and hospitals creates opportunities to target modifiable processes of care to standardize perioperative transfusion practice.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".