Assessing the diagnostic accuracy of symptoms and signs of degenerative cervical myelopathy: A prospective study
Bibliographic record
Abstract
Degenerative cervical myelopathy (DCM) is a clinical diagnosis based on history, physical exam, and imaging, but standardized criteria have not been established, contributing to diagnostic delays. We conducted a prospective study of DCM and healthy subjects that comprehensively evaluated symptoms, patient-reported and clinician-administered outcome measures, and physical assessments of motor and sensory function.Diagnostic utility was evaluated using Youden's Index (YI=sensitivity+specificity-1). 139 DCM patients and 108 age-matched healthy subjects were compared. Distinguishing symptoms included neck pain (YI=63%), upper extremity (UE) numbness (YI=57%), hand clumsiness (YI=50%), walking imbalance (YI=50%), and UE weakness (YI=46%). Questionnaires performed well including mJOA (YI=72%), NDI (YI=63%), and EQ-5D (YI=57%). Physical testing showed best results with UE reflexes (YI=54%), strength in 5 UE muscle groups (YI=53%), Berg Balance scale (YI=50%), self-paced walking velocity (YI=48%), and tandem gait assessment (YI=40%). Hand dexterity, strength dynamometry, and testing of 5 sensory modalities demonstrated poor diagnostic utility. Diagnosis of DCM is challenging, but key symptoms include neck pain, UE weakness, and those captured by the mJOA (particularly UE numbness, hand clumsiness, and walking imbalance). Physical testing of reflexes, manual motor testing, and gait/balance are useful to confirm the diagnosis. These findings offer guidance for clinicians and the development of diagnostic criteria.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".