Normalizing spinal cord compression measures in degenerative cervical myelopathy
Bibliographic record
Abstract
BACKGROUND CONTEXT: Accurate and automatic MRI measurements are relevant for assessing spinal cord compression severity in degenerative cervical myelopathy (DCM) and guiding treatment. The widely-used maximum spinal cord compression (MSCC) index has limitations. Firstly, it normalizes the anteroposterior cord diameter by that above and below the compression but does not account for cord size variation along the superior-inferior axis, making MSCC sensitive to compression level. Secondly, cord shape varies across individuals, making MSCC sensitive to this variability. Thirdly, MSCC is typically calculated by an expert-rater from a single sagittal slice, which is time-consuming and prone to variability. PURPOSE: This study proposes a fully automatic pipeline to compute MSCC. DESIGN: We developed a normalization strategy for traditional MSCC (anteroposterior diameter) using a healthy adults database (n = 203) to address cord anatomy variability across individuals and evaluated additional morphometrics (transverse diameter, area, eccentricity, and solidity). PATIENT SAMPLE: DCM patient cohort of n = 120. OUTCOME MEASURES: Receiver operating characteristic (ROC) and area under the curve (AUC) were used as evaluation metrics. METHODS: We validated the method in a mild DCM patient cohort against manually derived morphometrics and predicted the therapeutic decision (operative/conservative) using a stepwise binary logistic regression incorporating demographics and clinical scores. RESULTS: The automatic and normalized MSCC measures correlated significantly with clinical scores and predicted the therapeutic decision more accurately than manual MSCC. Significant predictors included upper extremity sensory dysfunction, T2w hyperintensity, and the proposed MRI-based measures. The model achieved an area under the curve of 0.80 in receiver operating characteristic analysis. CONCLUSION: This study introduced an automatic method for computing normalized measures of cord compressions from MRIs, potentially improving therapeutic decisions in DCM patients. The method is open-source and available in Spinal Cord Toolbox v6.0 and above.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".