Predicting Allogeneic Blood Transfusion Use in Total Joint Arthroplasty
Bibliographic record
Abstract
In Brief Total joint arthroplasty (TJA) patients often receive allogeneic blood transfusion. In this study we sought to create and validate a clinical prediction rule for transfusion in TJA using data that are easily available when scheduling the procedure. Logistic regression modeling was applied to retrospective data from all TJA procedures performed in Edmonton, Alberta in 2000 (n = 1875). The area under the receiver operating curve for the resulting model in the training and validation data sets was 0.80 and 0.76 respectively. By assigning a simple score based on six independent predictors (age, gender, weight, hemoglobin, ASA operative risk classification and whether revision surgery was planned), it was possible to classify a given subject’s risk of receiving allogeneic transfusion. We conclude that accurate prediction of transfusion risk in TJA is possible using a rule based on simple preoperative clinical and laboratory data. Such prediction could allow transfusion prevention strategies to be applied selectively to those at greatest risk. IMPLICATIONS: The use of allogeneic blood in patients undergoing joint replacement surgery was modeled statistically. A prediction rule was created from this model. It estimates a given patient’s risk of transfusion during total joint arthroplasty and can be used to target transfusion risk reduction measures more effectively.
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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 | 0.001 | 0.009 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".