Machine learning-based cluster analysis identifies four unique phenotypes of patients with degenerative cervical myelopathy with distinct clinical profiles and long-term functional and neurological outcomes
Bibliographic record
Abstract
BACKGROUND: Degenerative cervical myelopathy (DCM), the predominant cause of spinal cord dysfunction among adults, exhibits diverse interrelated symptoms and significant heterogeneity in clinical presentation. This study sought to use machine learning-based clustering algorithms to identify distinct patient clinical profiles and functional trajectories following surgical intervention. METHODS: In this study, we applied k-means and latent profile analysis (LPA) to identify patient phenotypes, using aggregated data from three major DCM trials. The combination of Nurick score, NDI (neck disability index), neck pain, as well as motor and sensory scores facilitated clustering. Goodness-of-fit indices were used to determine the optimal cluster number. ANOVA and post hoc Tukey's test assessed outcome differences, while multinomial logistic regression identified significant predictors of group membership. FINDINGS: A total of 1047 patients with DCM (mean [SD] age: 56.80 [11.39] years, 411 [39%] females) had complete one year outcome assessment post-surgery. Latent profile analysis identified four DCM phenotypes: "severe multimodal impairment" (n = 286), "minimal impairment" (n = 116), "motor-dominant" (n = 88) and "pain-dominant" (n = 557) groups. Each phenotype exhibited a unique symptom profile and distinct functional recovery trajectories. The "severe multimodal impairment group", comprising frail elderly patients, demonstrated the worst overall outcomes at one year (SF-36 PCS mean [SD]: 40.01 [9.75]; SF-36 MCS mean [SD], 46.08 [11.50]) but experienced substantial neurological recovery post-surgery (ΔmJOA mean [SD]: 3.83 [2.98]). Applying the k-means algorithm yielded a similar four-class solution. A higher frailty score and positive smoking status predicted membership in the "severe multimodal impairment" group (OR 1.47 [95% CI 1.07-2.02] and 1.58 [95% CI 1.25-1.99, respectively]), while undergoing anterior surgery and a longer symptom duration were associated with the "pain-dominant" group (OR 2.0 [95% CI 1.06-3.80] and 3.1 [95% CI 1.38-6.89], respectively). INTERPRETATION: Unsupervised learning on multiple clinical metrics predicted distinct patient phenotypes. Symptom clustering offers a valuable framework to identify DCM subpopulations, surpassing single patient reported outcome measures like the mJOA. FUNDING: No funding was received for the present work. The original studies were funded by AO Spine North America.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".