Degenerative Cervical Myelopathy: Towards a Personalized Approach
Bibliographic record
Abstract
Degenerative cervical myelopathy (DCM) is a recently coined term encompassing a variety of age-related and genetically associated pathologies, including cervical spondylotic myelopathy, degenerative disc disease, and ligamentous aberrations such as ossification of the posterior longitudinal ligament. All of these pathologies produce chronic compression of the spinal cord causing a clinical syndrome characterized by decreased hand dexterity, gait imbalance, and potential genitourinary or sensorimotor disturbances. Substantial variability in the underlying etiology of DCM and its natural history has generated heterogeneity in practice patterns. Ongoing debates in DCM management most commonly center around clinical decision-making, timing of intervention, and the ideal surgical approach. Pivotal basic science studies during the past two decades have deepened our understanding of the pathophysiologic mechanisms surrounding DCM. Growing knowledge of the key pathophysiologic processes will help us tailor personalized approaches in an increasingly heterogeneous patient population. This article focuses on summarizing the most exciting approaches in personalizing DCM patient treatments including biomarkers, factors affecting clinical decision-making, and choice of the optimal surgical approach. Throughout we provide a concise review on the conditions encompassing DCM and discuss the underlying pathophysiology of chronic spinal cord compression. We also provide an overview on clinical-radiologic diagnostic modalities as well as operative and nonoperative treatment strategies, thereby addressing knowledge gaps and controversies in the field of DCM.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".