Myelopathy and Spinal Deformity
Bibliographic record
Abstract
Surgical management of degenerative cervical myelopathy requires careful pathoanatomic consideration to select between various surgical options from both anterior and posterior approach. Hitherto, unexplored is the relevance of cervical deformity to the pathophysiology of such neurological disability, and whether correction of that deformity should be a surgical objective when planning for reconstruction after spinal cord decompression. Such correction could address both the static cord compression and the dynamic repetitive cord injury, while also restoring more normal biomechanics to the cervical spine. The articles in this focus issue's section on cervical spinal deformity reveal that cervical sagittal alignment is geometrically related to thoracolumbar spinal pelvic alignment and to T1 slope, and that it is further clinically correlated to regional disability and general health scores and to myelopathy severity. These conclusions are based on narrative reviews and a selection of primary research data, reflecting the nascency of this field. They further recommend for preoperative assessment of spinal alignment when significant deformity is suspected, and that correction of cervical kyphosis should be an objective when surgery is planned.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".