Focal compression of the cervical spinal cord alone does not indicate high risk of neurological deterioration in patients with a diagnosis of mild degenerative cervical myelopathy
Bibliographic record
Abstract
Degenerative Cervical Myelopathy (DCM) is the functional derangement of the spinal cord resulting from vertebral column spondylotic degeneration. Typical neurological symptoms of DCM include gait imbalance, hand/arm numbness, and upper extremity dexterity loss. Greater spinal cord compression is believed to lead to a higher rate of neurological deterioration, although clinical experience suggests a more complex mechanism involving spinal canal diameter (SCD). In this study, we utilized machine learning clustering to understand the relationship between SCD and different patterns of cord compression (i.e. compression at one disc level, two disc levels, etc.) to identify patient groups at risk of neurological deterioration. 124 MRI scans from 51 non-operative DCM patients were assessed through manual scoring of cord compression and SCD measurements. Dimensionality reduction techniques and k-means clustering established patient groups that were then defined with their unique risk criteria. We found that the compression pattern is unimportant at SCD extremes (≤14.5 mm or > 15.75 mm). Otherwise, severe spinal cord compression at two disc levels increases deterioration likelihood. Notably, if SCD is normal and cord compression is not severe at multiple levels, deterioration likelihood is relatively reduced, even if the spinal cord is experiencing compression. We elucidated five patient groups with their associated risks of deterioration, according to both SCD range and cord compression pattern. Overall, SCD and focal cord compression alone do not reliably predict an increased risk of neurological deterioration. Instead, the specific combination of narrow SCD with multi-level focal cord compression increases the likelihood of neurological deterioration in mild DCM patients.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".