A Practical Classification System for Acute Cervical Spinal Cord Injury Based on a Three-Phased Modified Delphi Process From the AOSpine Spinal Cord Injury Knowledge Forum
Bibliographic record
Abstract
STUDY DESIGN: A modified Delphi study. OBJECTIVE: To assess current practice patterns in the management of cervical spinal cord injury (SCI) and develop a simplified, practical classification system which offers ease of use in the acute setting, incorporates modern diagnostic tools and provides utility in determining treatment strategies for cervical SCI. METHODS: A three-phase modified Delphi procedure was performed between April 2020 and December 2021. During the first phase, members of the AOSpine SCI Knowledge forum proposed variables of importance for classifying and treating cervical SCI. The second phase involved an international survey of spine surgeons gauging practices surrounding the role and timing of surgery for cervical SCI and opinions regarding factors which most influence these practices. For the third phase, information obtained from phases 1 and 2 were used to draft a new classification system. RESULTS: 396 surgeons responded to the survey. Neurological status, spinal stability and cord compression were the most important variables influencing decisions surrounding the role and timing of surgery. The majority (>50%) of respondents preferred to perform surgery within 24 hours post-SCI in clinical scenarios in which there was instability, severe cord compression or severe neurology. Situations in which <50% of respondents were inclined to operate early included: SCI with mild neurological impairments, with cord compression but without instability (with or without medical comorbidities), and SCI without cord compression or instability. CONCLUSIONS: Spinal stability, cord compression and neurological status are the most important variables influencing surgeons' practices surrounding the surgical management of cervical SCI. Based on these results, a simplified classification system for acute cervical SCI has been proposed.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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".