Optimizing the Application of Surgery for Degenerative Cervical Myelopathy [AO Spine RECODE-DCM Research Priority Number 10]
Bibliographic record
Abstract
STUDY DESIGN: Literature Review (Narrative). OBJECTIVE: To introduce the number 10 research priority for Degenerative Cervical Myelopathy: Individualizing Surgery. METHODS: This article summarizes the current recommendations and indications for surgery, including how known prognostic factors such as injury time, age, disease severity, and associated comorbidities impact surgical outcome. It also considers key areas of uncertainty that should be the focus of future research. RESULTS: While a small proportion of conservatively managed patients may remain stable, the majority will deteriorate over time. To date, surgical decompression is the mainstay of treatment, able to halt disease progression and improve neurologic function and quality of life for most patients. Whilst this recognition has led to recommendations on when to offer surgery, there remain many uncertainties including the type of surgery, or timing in milder and/or asymptomatic cases. Their clarification has the potential to transform outcomes, by ensuring surgery offers each individual its maximum benefit. CONCLUSION: Developing the evidence to better guide surgical decision-making at the individual patient level is a research priority for Degenerative Cervical Myelopathy.
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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 | 0.011 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".