Predictors of Blood Transfusion in Posterior Lumbar Spinal Fusion
Bibliographic record
Abstract
STUDY DESIGN: Retrospective cohort study. OBJECTIVE: To identify patient or procedure related predictors of postoperative blood transfusions in posterior lumbar fusion (PSF). SUMMARY OF BACKGROUND DATA: The rate of PSF surgery has increased significantly. It remains the most common surgical procedure used to stabilize the spine; however, the impact of blood loss requiring blood transfusions remains a significant concern. METHODS: Analysis of data from the Canadian Spine Outcomes and Research Network. Patients who underwent PSF between 2008 and 2015 were identified. Multivariate analysis was used to identify predictors of blood transfusion from the collected information. RESULTS: Seven hundred seventy two patients have undergone PSF, 18% required blood transfusion, 54.8% were females and the mean age was 60 years. The analysis revealed five significant predictors: American Society of Anesthesiologist class (ASA), operative time, multilevel fusion, sacrum involvement, and open posterior approach. The odds of transfusion for those with ASA >1 were 6 times those with ASA1 (odds ratio [OR] 6.1, 95% confidence interval [CI] 1.4-27.1, P < 0.018). For each 60-minute increase in operative time, the odds of transfusion increased by 4.2% (OR 1.007, 95% CI 1.004-1.009, P < 0.001). The odds of transfusion were 6 times higher for multilevel fusion (OR 5.8, 95% CI 2.6-13.2, P < 0.001). Extending fusion to the sacrum showed 3 times higher odds for blood transfusion (OR 3.2, 95% CI 1.8-5.8, P < 0.001). The odds of transfusion for patients undergoing open approach were 12 times those who had minimal invasive surgery (OR 12.5, 95% CI 1.6-97.4, P < 0.016). Finally, patients receiving transfusions were more likely to have extended hospital stay. CONCLUSION: ASA >1, prolonged operative time, multilevel fusion, sacrum involvement, and open posterior approach were significant predictors of blood transfusion in PSF. LEVEL OF EVIDENCE: 3.
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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) | 0.001 | 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) | 0.001 | 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 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".