State of the Art in Degenerative Cervical Myelopathy: An Update on Current Clinical Evidence
Bibliographic record
Abstract
Degenerative cervical myelopathy (DCM) is a common cause of spinal cord dysfunction that confronts clinicians on a daily basis. Research performed over the past few decades has provided improved insight into the diagnosis, evaluation, and treatment of this disorder. We aim to provide clinicians with an update regarding the state of the art in DCM, focusing on more recent research pertaining to pathophysiology, natural history, treatment, consideration of the minimally symptomatic patient, surgical outcome prediction, and outcome measurement. Current concepts of pathophysiology focus on the combination of static and dynamic elements leading to breakdown of the blood-spinal cord barrier at the site of compression resulting in local inflammation, cellular dysfunction, and apoptosis. With respect to treatment, although there is a dearth of high-quality studies comparing surgical to nonoperative treatment, several large prospective studies have recently associated surgical management with clinically and statistically significant improvement in functional, disability, and quality of life outcome at long-term follow-up. When selecting the specific surgical intervention for a patient with DCM, anterior (discectomy, corpectomy, hybrid discectomy/corpectomy), posterior (laminectomy and fusion, laminoplasty), and combined approaches may be considered as options depending on the specifics of the patient in question; evidence supporting each of these approaches is reviewed in detail. Recently developed clinical prediction models allow for accurate forecasting of postoperative outcomes, permitting enhanced communication and management of patient expectations in the preoperative setting. Finally, an overview of outcome measures recommended for use in the assessment of DCM patients is provided.
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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".