Diagnosis, Heritability, and Outcome Assessment in Cervical Myelopathy
Bibliographic record
Abstract
This section of the cervical spondylotic myelopathy (CSM) Spine focus issue collates evidence related to diagnosis, outcome assessment, and genetics. Given that a variety of different disease states can present similarly, a guide for diagnosing and differentiating CSM from other neurological conditions is initially presented. Although the value of magnetic resonance imaging in diagnosing CSM is cemented, its value as a tool to predict future outcome is less well established. To this end, the existing evidence suggests that although increased T2 cord signal is of limited value, the pairing of high T2 signal with low T1 signal, or a high T2 to T1 signal ratio, is associated with a reduced potential for neurological recovery at follow-up. Outcome assessment in CSM is of paramount importance when monitoring patients' clinical course or measuring the efficacy of therapeutic interventions. Here, the main outcome measures that have been used to assess patients with CSM are reviewed. At present, we recommend that clinicians acquire the modified Japanese Orthopaedic Association scale score and the Neck Disability Index on all patients with CSM at presentation and follow-up. Finally, in regard to genetics, the existing evidence seems to support the principle of an inherited predisposition to both CSM and ossification of the posterior longitudinal ligament. Although several genetic polymorphisms have been consistently associated with ossification of the posterior longitudinal ligament, no specific polymorphisms were consistently associated with CSM.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".