Preoperative Computed Tomography Myelography Parameters as Predictors of Outcome in Patients With Degenerative Cervical Myelopathy: Results of a Systematic Review
Bibliographic record
Abstract
Study Design: Systematic review. Objectives: To determine the preoperative computed tomography (CT) myelogram imaging parameters in patients diagnosed with degenerative cervical myelopathy (DCM) that correlate with severity of DCM and predict postoperative patients’ functional outcome. Methods: An electronic database search was performed using Ovid Medline and Embase. CT myelogram studies investigating the correlation between imaging characteristics and DCM severity or postoperative outcomes were included. Two independent reviewers performed citation screening, selection, qualitative assessment, and data extraction using an objective and blinded protocol. Results: A total of 5 studies (402 patients) were included in this review and investigated the role of preoperative CT myelogram parameters in predicting the functional outcome after surgical treatment of DCM. All studies were retrospective cohort studies. CT myelogram characteristics included the transverse area of the spinal cord at maximum level of compression, spinal canal narrowing, number of blocks, spinal canal diameter, and flattening ratio. There is low evidence suggesting that patients with a preoperative transverse area of the spinal cord >30 mm 2 at the level of maximum compression have better postoperative recovery and outcome. We found no studies investigating the correlation between preoperative CT myelogram parameters and DCM severity. Conclusions: Patients with greater transverse area of spinal cord at the level of maximum compression on the preoperative CT myelogram are more likely to have better neurological outcome after surgery. There is insufficient evidence to suggest that any of the other CT myelogram parameters investigated are predictors of postoperative outcomes in patients with DCM.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".