Cervical Spondylotic Myelopathy
Bibliographic record
Abstract
STUDY DESIGN: Narrative overview of the focus issue on cervical spondylotic myelopathy (CSM). OBJECTIVE: To provide an introduction to this special focus issue of Spine journal that deals with CSM. SUMMARY OF BACKGROUND DATA: CSM is a progressive spine disease and the leading cause of spinal cord dysfunction worldwide. The shift in population demographics in many parts of the world has motivated the development of this focus issue. With the overall aging of the population, we can predict that global health care systems will be confronted with an increase in patients presenting with degenerative spine changes and various stages of cervical myelopathy. METHODS: The articles in this issue vary in their methods-there are systematic reviews, narrative reviews, a study protocol, and 2 primary research articles. RESULTS: A summary of the findings of each article is provided as a table within this introduction. CONCLUSION: By way of this issue, we have summarized current knowledge gaps and limitations in the evidence to provide direction for future research and investigation. These include the need for a unified nomenclature for referring to CSM, the need to specify the incidence of myelopathy and the prevalence of ossification of the posterior longitudinal ligament, and the need to explore both the safety and efficacy of neuroprotective and neuroreparative pharmacological strategies. Further work is also required to optimize the management of patients with minimal symptoms.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 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".