Propensity score matching analysis of the relationship between allogeneic blood transfusion and postoperative pulmonary complications in scoliosis correction surgery: a retrospective study
Bibliographic record
Abstract
Background Surgery is still the treatment of choice for patients with moderate to severe scoliosis, and vertebral column resection can significantly correct scoliosis. However, scoliosis correction surgery is associated with a high incidence of perioperative complications. We hypothesize that receiving allogeneic blood transfusions during surgery increases the risk of these complications in patients undergoing scoliosis correction surgery. Methods This retrospective study included 512 patients who underwent scoliosis correction surgery at the Second Hospital of Zhejiang University School of Medicine between August 2016 and April 2023. Patients who experienced or did not experience transfusion were balanced in terms of baseline clinicodemographic characteristics using propensity score matching. Multivariable logistic regression of the balanced data was performed to assess the potential influence of intraoperative allogeneic transfusion on incidence of PPCs. Results Propensity score matching led to a dataset of 322 patients, of whom 161 experienced allogeneic transfusion and 161 did not. Multifactorial logistic regression identified the following factors associated with PPCs: intraoperative allogeneic red blood cell transfusion rate (Risk Ratio (RR) 1.53 95% confidence interval (CI) 1.12–2.11, p = 0.007). The risk of PPCs increased with increasing volume of allogeneic blood transfusions, with those receiving 400 mL and more being at greater risk compared to those receiving no more than 400 mL (RR 1.40, 95% CI 1.04–1.89, p = 0.030). Subgroup analyses showed increased PPC risk in females, longer surgeries (>3 h), and patients without TXA use. Conclusion Intraoperative allogeneic red blood cell transfusion rate and volume during scoliosis correction surgery may be strongly associated with occurrence of PPCs.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".