Prediction of Massive Blood Loss in Scoliosis Surgery From Preoperative Variables
Bibliographic record
Abstract
STUDY DESIGN: A retrospective cohort study. OBJECTIVE: To identify predictors of massive blood loss after scoliosis surgery. SUMMARY OF BACKGROUND DATA: Scoliosis surgery may be associated with considerable blood loss. Many blood conservation techniques have been used to reduce the allogeneic transfusion. An ability to identify patients with high risk of massive blood loss preoperatively may be helpful for appropriate use of these techniques. METHODS: Data of patients undergoing scoliosis surgery between June 1, 2011, and October 31, 2011, were collected. Preoperative information and total blood loss, which was calculated as the sum of intraoperative and postoperative estimated blood loss, were recorded. Patients were divided into 2 groups, retrospectively: group A (n = 95) with the total blood loss more than 30% of estimated blood volume and group B (n = 64) with the total blood loss of 30% of estimated blood volume or less. Preoperative data were compared between the groups. Significant variables were selected for a forward stepwise binary logistic regression analysis to determine the independent risk factors of massive blood loss. RESULTS: More than half of the patients (59.7%) undergoing scoliosis surgery had massive blood loss. Patients in group A were shorter (P = 0.01) and had larger preoperative Cobb angle (P < 0.01), more levels fused (P < 0.01), and more osteotomies (P < 0.01) than those in group B. Preoperative Cobb angle more than 50º (P = 0.017, odds ratio = 2.47, 95% confidence interval: 1.17-5.20), the number of fused levels more than 6 (P = 0.014, odds ratio = 3.70, 95% confidence interval: 1.31-10.49), and osteotomy (P = 0.000, odds ratio = 4.64, 95% confidence interval: 1.97-10.94) were determined to be the independent risk factors of massive blood loss in scoliosis surgery. CONCLUSION: Risk of massive blood loss (total blood loss > 30% of estimated blood volume) in patients with scoliosis could increase, if they (1) had preoperative Cobb angle larger than 50º or (2) planned to undergo osteotomy or fusion of more than 6 levels.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 teacher head, 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".