Improving Awareness Could Transform Outcomes in Degenerative Cervical Myelopathy [AO Spine RECODE-DCM Research Priority Number 1]
Bibliographic record
Abstract
STUDY DESIGN: Literature Review (Narrative). OBJECTIVE: To introduce the number one research priority for Degenerative Cervical Myelopathy (DCM): Raising Awareness. METHODS: Raising awareness has been recognized by AO Spine RECODE-DCM as the number one research priority. This article reviews the evidence that awareness is low, the potential drivers, and why this must be addressed. Case studies of success from other diseases are also reviewed, drawing potential parallels and opportunities for DCM. RESULTS: DCM may affect as many as 1 in 50 adults, yet few will receive a diagnosis and those that do will wait many years for it. This leads to poorer outcomes from surgery and greater disability. DCM is rarely featured in healthcare professional training programs and has received relatively little research funding (<2% of Amyotrophic Lateral Sclerosis or Multiple Sclerosis over the last 25 years). The transformation of stroke and acute coronary syndrome services, from a position of best supportive care with occasional surgery over 50 years ago, to avoidable disability today, represents transferable examples of success and potential opportunities for DCM. Central to this is raising awareness. CONCLUSION: Despite the devastating burden on the patient, recognition across research, clinical practice, and healthcare policy are limited. DCM represents a significant unmet need that must become an international public health priority.
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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 | 0.009 | 0.057 |
| 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 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".