A Quantitative and Reproducible Method to Assess Cord Compression and Canal Stenosis After Cervical Spine Trauma
Bibliographic record
Abstract
STUDY DESIGN: Reliability study. OBJECTIVE: To assess the intrarater and interrater reliability of a recently described technique to measure of maximum canal compromise (MCC) and maximum spinal cord compression (MSCC) using digitized and magnified images in the setting of traumatic cervical spinal cord injury (SCI). SUMMARY OF BACKGROUND DATA: The extent of MCC and MSCC is of clinical and prognostic value in the setting of traumatic cervical SCI. However, concerns remain regarding the accuracy of measurements based on hard copy images. We hypothesized that the interrater and intrarater reliability of these assessments would be enhanced using magnified digitized images and software-based measurement tools. METHODS: Midsagittal MRI and CT images of cervical spine were selected from 5 individuals with acute traumatic cervical SCI. Measurements of MCC using CT scan and T1-weighted MRI and measurements of MSCC based on T2-weighted MR images were independently estimated by 13 raters on 10 occasions. RESULTS: The intrarater reliability for CT-MCC, T1-weighted MRI-MCC and T2-weighted MRI-MSCC was high in the 10 rounds in each patient. In addition, the mean intrarater interclass correlation coefficient was 0.72 +/- 0.05 for the CT-MCC, 0.70 +/- 0.07 for the T1-weighted MRI-MCC, and 0.68 +/- 0.11 for the T2-weighted MRI-MSCC. The mean interrater interclass correlation coefficients were 0.43 +/- 0.02 for the CT-MCC, 0.61 +/- 0.03 for the T1-weighted MRI-MCC, and 0.55 +/- 0.05 for the evaluation of T2-weighted MRI-MSCC. CONCLUSION: Our study has demonstrated that the intrarater reliability for the instrument to assess MCC and MSCC in the setting of traumatic SCI was high. The interrater ICCs at a moderate level of reliability combined with our results using analysis of variance with post hoc tests indicate that the measurements of MCC and MSCC are reproducible, which supports the use of these radiologic parameters in the clinical and research settings.
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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 | 0.023 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".