Redefining Clinically Significant Blood Loss in Complex Adult Spine Deformity Surgery
Bibliographic record
Abstract
STUDY DESIGN: Retrospective analysis of prospectively collected data. OBJECTIVE: This study aims to define clinically relevant blood loss in adult spinal deformity (ASD) surgery. BACKGROUND: Current definitions of excessive blood loss after spine surgery are highly variable and may be suboptimal in predicting adverse events (AEs). MATERIALS AND METHODS: Adults undergoing complex ASD surgery were included. Estimated blood loss (EBL) was extracted for investigation, and estimated blood volume loss (EBVL) was calculated by dividing EBL by the preoperative blood volume utilizing Nadler's formula. "Least Absolute Shrinkage and Selection Operator" regression was performed to identify 5 variables from demographic and perioperative parameters. Logistic regression was subsequently performed to generate a receiver operating characteristic curve and estimate an optimal threshold for EBL and EBVL. Finally, the proportion of patients with AE was plotted against EBL and EBVL to confirm the identified thresholds. RESULTS: In total, 552 patients were included with a mean age of 60.7 ± 15.1 years, 68% females, mean Charlson Comorbidity Index was 1.0 ± 1.6, and 22% experienced AEs. Least Absolute Shrinkage and Selection Operator regression identified the American Society of Anesthesiologists score, baseline hypertension, preoperative albumin, and use of intraoperative crystalloids as the top predictors of an AE, in addition to EBL/EBVL. Logistic regression resulted in the receiver operating characteristic curve, which was used to identify a cutoff of 2.3 L of EBL and 42% for EBVL. Patients exceeding these thresholds had AE rates of 36% (odds ratio: 2.1, 95% CI: 1.2-3.6) and 31% (odds ratio: 1.7, 95% CI: 1.1-2.8), compared with 21% for those below the thresholds of EBL and EBVL, respectively. CONCLUSION: In complex ASD surgery, intraoperative EBL of 2.3 L and an EBVL of 42% are associated with clinically significant AEs. These thresholds may be useful in guiding preoperative-patient-counseling, health care system quality initiatives, and clinical perioperative blood loss management strategies in patients undergoing complex spine surgery. In addition, a similar methodology could be performed in other specialties to establish procedure-specific clinically relevant blood loss thresholds.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".