Does Magnetic Resonance Imaging Improve the Predictive Performance of a Validated Clinical Prediction Rule Developed to Evaluate Surgical Outcome in Patients With Degenerative Cervical Myelopathy?
Bibliographic record
Abstract
STUDY DESIGN: Ambispective study. OBJECTIVE: To determine whether MRI parameters improve the predictive performance of a validated clinical prediction rule used to assess functional outcomes in surgical patients with DCM. SUMMARY OF BACKGROUND DATA: Degenerative cervical myelopathy (DCM) is the most common cause of spinal cord dysfunction in the elderly worldwide. A clinical prediction rule was developed to discriminate between patients with mild myelopathy postoperatively (mJOA ≥ 16) and those with substantial residual neurological impairment (mJOA < 16). Recently, a separate magnetic resonance imaging (MRI)-based prediction model was created. However, a model exploring the combined predictive value of imaging and clinical variables does not exist. METHODS: One hundred and fourteen patients with MRIs were examined from a cohort of 278 patients enrolled in the AOSpine CSM-North America Study. Ninety-nine patients had complete preoperative imaging and postoperative outcome data. MRIs were evaluated for the presence/absence of signal change on T2- and T1-weighted images. Quantitative analysis of the T2 signal change was conducted and maximum canal compromise and cord compression were calculated. The added predictive performance of each MRI parameter to the clinical model was evaluated using receiver operator characteristic curves. RESULTS: The model developed on our subsample yielded an area under the receiver operator curve (AUC) of 0.811 (95% CI: 0.726-0.896). The addition of imaging variables did not significantly improve the predictive performance. Small improvements in prediction were obtained when sagittal extent of T2 hyperintensity (AUC: 0.826, 95% CI: 0.743-0.908, 1.35% increase) or Wang ratio (AUC: 0.823, 95% CI: 0.739-0.907, 1.21%) was added. Anatomic characteristics, such as maximum canal compromise and maximum cord compression, did not improve the discriminative ability of the clinical prediction model. CONCLUSION: In our sample of surgical patients, with clinical and image-evidence of DCM, MRI parameters do not significantly add to the predictive performance of a previously published clinical prediction rule. It remains plausible that combinations of the strongest clinical and MRI predictors may yield a similar or a superior prediction model. LEVEL OF EVIDENCE: 3.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".