Abstract 20153: Predicting the Need for Transfusions in Endovascular Aneurysm Repair (EVAR)
Bibliographic record
Abstract
Purpose: The majority of patients undergoing EVAR do not require blood transfusion, yet blood type and screen (T&S) is routinely performed. Identifying patients in whom T&S can be avoided presents a substantial cost saving opportunity. Hypothesis: We hypothesized that intraoperative blood transfusions can be predicted preoperatively. Methods: Using the Vascular Study Group of New England database from 2003-2014, we performed a retrospective review of 4700 patients who underwent EVAR. The cohort was split randomly into a training (60%) and validation (40%) set. A backwards logistic regression analysis was performed to identify predictors of intraoperative blood transfusion in the training set. The model was then tested in the validation set to estimate the receiver operating curves (ROC) and goodness of fit. Results: Preoperative hemoglobin, urgency (elective, symptomatic, or ruptured), age, maximal anterior-posterior AAA diameter, female gender, and history of CHF (asymptomatic, mild, moderate, or severe) were all significant predictors of intraoperative blood transfusions. The c-statistic for our model was .82 in the training set and .84 in the validation set, and the Hosmer-Lemenshow goodness-of-fit statistic was 0.99. Conclusions: Intraoperative blood transfusions can be routinely predicted preoperatively. Avoidance of T&S in low risk populations provides a substantial cost-saving opportunity.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".