We Choose to Call it ‘Degenerative Cervical Myelopathy’: Findings of AO Spine RECODE-DCM, an International and Multi-Stakeholder Partnership to Agree a Standard Unifying Term and Definition for a Disease
Bibliographic record
Abstract
STUDY DESIGN: Modified DELPHI Consensus Process. OBJECTIVE: To agree a single unifying term and definition. Globally, cervical myelopathy caused by degenerative changes to the spine is known by over 11 different names. This inconsistency contributes to many clinical and research challenges, including a lack of awareness. METHOD: AO Spine RECODE-DCM (Research objectives and Common Data Elements Degenerative Cervical Myelopathy). To determine the index term, a longlist of candidate terms and their rationale, was created using a literature review and interviews. This was shared with the community, to select their preferred terms (248 members (58%) including 149 (60%) surgeons, 45 (18%) other healthcare professionals and 54 (22%) People with DCM or their supporters) and finalized using a consensus meeting. To determine a definition, a medical definition framework was created using inductive thematic analysis of selected International Classification of Disease definitions. Separately, stakeholders submitted their suggested definition which also underwent inductive thematic analysis (317 members (76%), 190 (59%) surgeons, 62 (20%) other healthcare professionals and 72 (23%) persons living with DCM or their supporters). Using this definition framework, a working definition was created based on submitted content, and finalized using consensus meetings. RESULTS: Degenerative Cervical Myelopathy was selected as the unifying term, defined in short, as a progressive spinal cord injury caused by narrowing of the cervical spinal canal. CONCLUSION: A consistent term and definition can support education and research initiatives. This was selected using a structured and iterative methodology, which may serve as an exemplar for others in the future.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".