Intramedullary Lesion Length on Postoperative Magnetic Resonance Imaging is a Strong Predictor of ASIA Impairment Scale Grade Conversion Following Decompressive Surgery in Cervical Spinal Cord Injury
Bibliographic record
Abstract
BACKGROUND: Evidence indicates that, over time, patients with spinal cord injury (SCI) improve neurologically in various degrees. We sought to further investigate indicators of grade conversion in cervical SCI. OBJECTIVE: To detect predictors of ASIA impairment scale (AIS) grade conversion in SCI following surgical decompression. METHODS: In a retrospective study, demographics, clinical, imaging, and surgical data from 100 consecutive patients were assessed for predictors of AIS grade conversion. RESULTS: American Spinal Injury Association motor score was 17.1. AIS grade was A in 52%, B in 29%, and C in 19% of patients. Surgical decompression took place on an average of 17.6 h following trauma (≤12 h in 51 and >12 h in 49). Complete decompression was verified by magnetic resonance imaging (MRI) in 73 patients. Intramedullary lesion length (IMLL) on postoperative MRI measured 72.8 mm, and hemorrhage at the injury epicenter was noted in 71 patients. Grade conversion took place in 26.9% of AIS grade A patients, 65.5% of AIS grade B, and 78.9% of AIS grade C. AIS grade conversion had statistical relationship with injury severity score, admission AIS grade, extent of decompression, presence of intramedullary hemorrhage, American Spinal Injury Association motor score, and IMLL. A stepwise multiple logistic regression analysis indicated IMLL was the sole and strongest indicator of AIS grade conversion (odds ratio 0.950, 95% CI 0.931-0.969). For 1- and 10-mm increases in IMLL, the model indicates 4% and 40% decreases, respectively, in the odds of AIS grade conversion. CONCLUSION: Compared with other surrogates, IMLL remained as the only predictor of AIS grade conversion.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 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".