Establishing Diagnostic Criteria for Degenerative Cervical Myelopathy [AO Spine RECODE-DCM Research Priority Number 3]
Bibliographic record
Abstract
STUDY DESIGN: Narrative review. OBJECTIVES: To discuss the importance of establishing diagnostic criteria in Degenerative Cervical Myelopathy (DCM), including factors that must be taken into account and challenges that must be overcome in this process. METHODS: Literature review summarising current evidence of establishing diagnostic criteria for DCM. RESULTS: Degenerative Cervical Myelopathy (DCM) is characterised by a degenerative process of the cervical spine resulting in chronic spinal cord dysfunction and subsequent neurological disability. Diagnostic delays lead to progressive neurological decline with associated reduction in quality of life for patients. Surgical decompression may halt neurologic worsening and, in many cases, improves function. Therefore, making a prompt diagnosis of DCM in order to facilitate early surgical intervention is a clinical priority in DCM. CONCLUSION: There are often extensive delays in the diagnosis of DCM. Presently, no single set of diagnostic criteria exists for DCM, making it challenging for clinicians to make the diagnosis. Earlier diagnosis and subsequent specialist referral could lead to improved patient outcomes using existing treatment modalities.
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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 | 0.015 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.013 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".