Neurosurgery (Spine)
Bibliographic record
Abstract
Objective: The impact of spinal alignment on neurological recovery among myelopathy patients has not been thoroughly investigated. This study evaluated the impact of sagittal cervical alignment on neurological recovery in a prospective surgical series of myelopathy patients. Methods: Prospective data was analyzed from surgical CSM patients at a tertiary-care neurosurgical centre. Demographic data and clinical preoperative and postoperative measures of neurological disability (mJOA, Nurick, NDI scores) were analyzed for dependency on cervical spine imaging parameters. Results: Among 124 CSM patients, 34% exhibited kyphotic alignment. Surgical intervention was more frequently anterior or combined anterior/posterior among this group than those with preserved lordosis. Most patients exhibited postoperative neurological improvement for myelopathy severity, however the extent of this improvement was dichotomous based on preoperative sagittal alignment. Improvement was greater among patients with preoperative lordosis (ΔmJOA of 3.1) than those with preoperative kyphosis (ΔmJOA of 1.4, p=0.02). Surgical correction of spinal malalignment did not provide for heightened neurological recovery, although whether it protects against symptomatic adjacent segment disease is unclear. Conclusion: Most CSM patients showed postoperative neurological improvement. Patients with preoperative lordotic alignment exhibited greater improvement than those with preoperative kyphotic alignment. Neither correction of the spinal alignment nor surgical approach in this series specifically affected the extent of neurological recovery.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.043 | 0.018 |
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".