Role of Neck Pain in Defining Clinical Trajectories of Outcomes in Patients With Degenerative Cervical Myelopathy: Results of a Novel Machine Learning Algorithm
Bibliographic record
Abstract
Study DesignRetrospective analysis of prospective data.ObjectivesNeck pain represents a crucial factor underscoring a patient's decision to receive surgical intervention for degenerative cervical myelopathy (DCM). However, postoperative pain trajectories are poorly defined. This study aimed to employ machine learning-based trajectory modeling to identify patient subpopulations with distinct pain trajectories after surgery.MethodsWe pooled subjects from three major clinical studies on DCM. Group-based multivariate trajectory (GBMT) modeling was used to classify patients into distinct trajectories based on their neck pain score over one year. Outcome differences were examined with univariate analyses. Predictors of group membership were revealed with multinomial logistic regression.ResultsThree distinct trajectories of neck pain were identified from a total of 968 patients with DCM: "slow pain improvement" (n = 239; 25%), "no pain improvement" (n = 537; 55%), and "fast pain improvement" (n = 192; 20%) groups. Each trajectory exhibited a unique baseline pain profile. The "fast pain improvement" group, comprised of patients experiencing profound neck pain, had the best overall outcomes for pain, NDI, SF-36 PCS, and SF-36 MSC postoperatively. On the other hand, the "no pain improvement" group, consisting of patients with pain and multimodal impairment of moderate severity, had residual pain that remained constant and was least likely to experience functional outcome and quality of life improvement after one year.ConclusionsUnsupervised learning on neck pain identified unique pain recovery trajectories that consist of distinct patient phenotypes. Trajectory grouping offers an important framework to both identify novel DCM subpopulations and predict patterns of pain over time.Clinical Trials Included(1) Assessment of Surgical Techniques for Treating Cervical Spondylotic Myelopathy (CSM); https://clinicaltrials.gov/study/NCT00285337; ClinicalTrials.gov ID NCT00285337. (2) Surgical Treatment of Cervical Spondylotic Myelopathy; https://clinicaltrials.gov/study/NCT00565734; ClinicalTrals.gov ID NCT00565734. (3) Efficacy of Riluzole in Surgical Treatment for Cervical Spondylotic Myelopathy (CSM-Protect) (CSM-Protect); https://clinicaltrials.gov/study/NCT01257828; ClinicalTrials.gov ID NCT01257828.
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 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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".