Surgical Management of Degenerative Cervical Myelopathy
Bibliographic record
Abstract
Degenerative cervical myelopathy (DCM), including cervical spondylotic myelopathy and ossification of the posterior longitudinal ligament, presents a heterogeneous set of variables reflecting its complex nature. Multiple studies in the past have attempted to elucidate an ideal surgical algorithm that surgeons may use when treating these patients, unfortunately all studies to date, including the rigorous systematic review used in this focus issue, have fallen short in identifying a superior approach when addressing DCM. Likely because of a superior approach being nonexistent because there are multiple pathoanatomical considerations. In addition to the multitude of variables that spine surgeons face when deciding the treatment options for patients with DCM, the previous studies that have been published, unfortunately, lack in consistent outcome and complication reporting. Therefore, synthesizing a treatment algorithm remains difficult, however, the articles in this focus issue use the GRADE system to assess the overall quality (strength) of available evidence and, where appropriate, formulate evidence-based recommendations. Factors that should be included in surgical decision making are the sagittal alignment, anatomical location of the compressive pathology, number of levels of compression, presence of absence or instability or subluxation, the type compressive pathology (e.g., spondylosis vs. ossification of the posterior longitudinal ligament), neck anatomy, bone quality, and surgeon experience or preference. Fortunately, as reviewed in the accompanying articles, a number of excellent surgical options exist that can be selected on the basis of the aforementioned pathoanatomical considerations.
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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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".