Advancing Precision Medicine in Degenerative Cervical Myelopathy
Bibliographic record
Abstract
Degenerative cervical myelopathy (DCM) is the leading cause of nontraumatic spinal cord dysfunction and remains clinically heterogeneous in presentation and course. This review synthesizes current evidence on predictors of neurological outcomes across conventional prognostic factors (clinical and macrostructural metrics) and quantitative neuroimaging (microstructural metrics), as well as how machine learning (ML) models integrate these predictors into a precision medicine framework to aid in DCM management. We explore evidence on conventional clinical and radiographic factors. Although several signs and scales are associated with clinical outcomes, cross-study inconsistency and the limits of linear models blunt their standalone utility, underscoring the need for multifactorial modelling. We then assess quantitative MRI biomarkers, including diffusion tensor imaging, magnetization transfer, and myelin water imaging, which index axonal integrity and myelination, thereby enriching risk stratification and prediction. Building on these measurements, we examine ML models combining clinical, imaging, and demographic features to predict postoperative outcomes and, increasingly, the natural history of mild DCM. Finally, current gaps and necessary future directions are outlined, including protocol harmonization, prospective multicentre validation, and clinician-patient education to support equitable uptake. Collectively, this review advances in DCM diagnosis and prognosis, highlighting the role of precision medicine tools for personalized patient care.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".