P.089 A comparison of perioperative complication rates in adult spinal deformity correction with one versus two surgeons
Bibliographic record
Abstract
Background: Morbidity can be high in the management of adult spinal deformity patients. Complications include blood loss (EBL), durotomy, radicular pain, and postoperative hardware failure. Utilization of one versus two spinal surgeons in spinal deformity correction reduces overall perioperative morbidity is unclear. Methods: All procedures were performed by surgeons at a single institution between January 2012-2015. Patients were followed for a minimum of one year and maximum of four years. We retrospectively reviewed 60 cases of adult spinal deformity. Our cohort was divided into 1 versus 2 surgeons (12 vs 48 cases). We analyzed these cases for estimated blood loss and peri-operative complications. Results: Cases involving long thoracic to pelvis correction (T3-T6) was 20.8% in the 2 surgeons group and 8.3% in the 1 surgeon group. The EBL >3.0 L for 1 versus 2 surgeon groups were 25% and 41.6% respectively. Major complications in the 1 versus 2 surgeon group were 25% and 47.9% and the revision rates were 25% versus 37.5%. The percentage of minor complications in the 1 versus 2 surgeon group was 33.3% versus 14.6%. Conclusions: Utilizing two surgeons did not reduce complication rates. Procedures performed by two surgeons were more extensive deformity corrections. The extent of correction is the likely explanation for differing complication rates.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".