Prediction of Worse Functional Status After Surgery for Degenerative Cervical Myelopathy: A Machine Learning Approach
Bibliographic record
Abstract
BACKGROUND: Surgical decompression for degenerative cervical myelopathy (DCM) is one of the mainstays of treatment, with generally positive outcomes. However, some patients who undergo surgery for DCM continue to show functional decline. OBJECTIVE: To use machine learning (ML) algorithms to determine predictors of worsening functional status after surgical intervention for DCM. METHODS: This is a retrospective analysis of prospectively collected data. A total of 757 patients enrolled in 2 prospective AO Spine clinical studies, who underwent surgical decompression for DCM, were analyzed. The modified Japanese Orthopedic Association (mJOA) score, a marker of functional status, was obtained before and 1 yr postsurgery. The primary outcome measure was the dichotomized change in mJOA at 1 yr according to whether it was negative (worse functional status) or non-negative. After applying an 80:20 training-testing split of the dataset, we trained, optimized, and tested multiple ML algorithms to evaluate algorithm performance and determine predictors of worse mJOA at 1 yr. RESULTS: The highest-performing ML algorithm was a polynomial support vector machine. This model showed good calibration and discrimination on the testing data, with an area under the receiver operating characteristic curve of 0.834 (accuracy: 74.3%, sensitivity: 88.2%, specificity: 72.4%). Important predictors of functional decline at 1 yr included initial mJOA, male gender, duration of myelopathy, and the presence of comorbidities. CONCLUSION: The reasons for worse mJOA are frequently multifactorial (eg, adjacent segment degeneration, tandem lumbar stenosis, ongoing neuroinflammatory processes in the cord). This study successfully used ML to predict worse functional status after surgery for DCM and to determine associated predictors.
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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 | 0.001 |
| 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) | 0.000 | 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".