Predicting Outcomes After Surgical Decompression for Mild Degenerative Cervical Myelopathy: Moving Beyond the mJOA to Identify Surgical Candidates
Bibliographic record
Abstract
BACKGROUND: Patients with mild degenerative cervical myelopathy (DCM) represent a heterogeneous population, and indications for surgical decompression remain controversial. OBJECTIVE: To dissociate patient phenotypes within the broader population of mild DCM associated with degree of impairment in baseline quality of life (QOL) and surgical outcomes. METHODS: This was a post hoc analysis of patients with mild DCM (modified Japanese Orthopedic Association [mJOA] 15-17) enrolled in the AOSpine CSM-NA/CSM-I studies. A k-means clustering algorithm was applied to baseline QOL (Short Form-36 [SF-36]) scores to separate patients into 2 clusters. Baseline variables and surgical outcomes (change in SF-36 scores at 1 yr) were compared between clusters. A k-nearest neighbors (kNN) algorithm was used to evaluate the ability to classify patients into the 2 clusters by significant baseline clinical variables. RESULTS: One hundred eighty-five patients were eligible. Two groups were generated by k-means clustering. Cluster 1 had a greater proportion of females (44% vs 28%, P = .029) and symptoms of neck pain (32% vs 11%, P = .001), gait difficulty (57% vs 40%, P = .025), or weakness (75% vs 59%, P = .041). Although baseline mJOA correlated with neither baseline QOL nor outcomes, cluster 1 was associated with significantly greater improvement in disability (P = .003) and QOL (P < .001) scores following surgery. A kNN algorithm could predict cluster classification with 71% accuracy by neck pain, motor symptoms, and gender alone. CONCLUSION: We have dissociated a distinct patient phenotype of mild DCM, characterized by neck pain, motor symptoms, and female gender associated with greater impairment in QOL and greater response to surgery.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".