Perioperative Medical Complications in Adult Spine Deformity Surgery: Classification and Prevention Strategies
Bibliographic record
Abstract
Study DesignNarrative review.ObjectiveWe aimed to propose the classifications of, risk factors for, and prevention strategies for perioperative complications from previously published papers in adult spinal deformity (ASD) surgeries.MethodsA literature search was conducted in the PubMed/MEDLINE database to identify studies reporting perioperative complications of spinal deformity surgery, their classifications, risk factors, and prevention strategies. Main search terms included "perioperative complications", "medical complications" and "adult spinal deformity". In the various complications associated with deformity surgeries, we focused the medical complications which will directly link to patient's life prognosis rather than postoperative mechanical complications.ResultsThe overall perioperative complication rate ranged from 12.8% to 52.2%. Risk factors for perioperative medical complications included age, body mass index, osteoporosis, frailty, comorbidities, preoperative medication, smoking habit, and alcohol intake. Perioperative medical complications mostly depend on patient-related factors. Taking reference from the previous reported classification of surgical complications, the AO Spine Deformity Knowledge Forum developed a classification for complications of spinal deformity surgery via a Delphi exercise.ConclusionComplication classification system is important for understanding the impact of complications on patients. Further research should aim to determine the correlation between the new complication classification and patient outcomes in spinal deformity surgery. Various pre-, intra-, and postoperative strategies should be implemented to prevent perioperative complications.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".