P.090 The predictors of patient morbidity after adult spinal deformity correction: bone mineral density and the extent of deformity correction
Bibliographic record
Abstract
Background: Instrumentation failure (IF) such proximal junctional kyphosis/failure or distal junctional failure (PJK/PJF/DJF), rod fracture and screw-loosening can cause morbidity in patients with spinal deformity correction. Factors such as bone mineral density (BMD) or region of deformity correction may play a role in postoperative IF. Methods: We reviewed the relationship between IF and BMD or extent of spinal deformity. IF includes PJK/PJF/DJF, fractured rod, screw-looseing, radiculopathy, and non-union. BMD groups included Normal, osteopenia/osteoporosis, and Unknown. The extent of correction included Lumbar, Short Thoracolumbar (5-8 levels), Long Thoracolumbar (8 to 12 levels), and Cervical-thoracic. Results: 60 patients (41:19 F:M) were included, with average age of 65. Total IF=29 patients (48.3%). Normal BMD in N=14, with half of them (50.0%) developing IF; Low BMD in N=15, with one-third of them (33.3%) developing IF. Lumbar correction was performed in N=19, with IF in 36.8%; Short Thoracolumbar correction was performed in N=28, with IF in 46.4%; Long Thoracolumbar correction was performed in N=11, with IF in 81.8%; and Cervical correction in N=2, with no postoperative IF. Conclusions: Patients that received long-segment thoracolumbar had the highest rates of postoperative morbidity. We did not demonstrate an association between abnormal BMD and postoperative IF. A larger study would be needed for further investigations.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".