Impact of Mechanism of Injury on Long-term Neurological Outcomes of Cervical Sensorimotor Complete Acute Traumatic Spinal Cord Injury
Bibliographic record
Abstract
OBJECTIVE: Mechanism of injury is a largely understudied descriptor of acute traumatic spinal cord injury (tSCI). This study sought to compare the impact of high-energy and low-energy mechanisms of injury in neurological outcomes of cervical sensorimotor complete tSCI. METHODS: Patients with tSCI were identified in 4 prospective, multicenter clinical trials and registries. American Spinal Injury Association Impairment Scale (AIS) grade was assessed ≤ 72 hours postinjury and followed up between 12 to 52 weeks. Patients were included if they had a cervical and sensorimotor complete (AIS-A) injury at baseline. Study outcomes were change in AIS grade and lower extremity motor, upper extremity motor, and total motor scores. Propensity score matching between high-energy mechanisms of injury (HEMI; e.g. , motor vehicle collisions) and low-energy mechanisms of injury (LEMI; e.g. , falls) groups was performed. Adjusted groups were compared with paired t-tests and McNemar test. RESULTS: Of 667 patients eligible for inclusion, 523 experienced HEMI (78.4%). HEMI patients were younger, had lower body mass index, more associated fractures or dislocations, and lower baseline lower extremity motor scores. After propensity score matching of these baseline variables, 118 pairs were matched. HEMI patients had a significantly worse motor recovery from baseline to follow-up based on their diminished change in upper extremity motor scores and total motor scores. CONCLUSION: Cervical sensorimotor complete tSCIs from HEMI were associated with significantly lower motor recovery compared to LEMI patients. Our findings suggest that mechanism of injury should be considered in modelling prognosis and in understanding the heterogeneity of outcomes after acute tSCI.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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 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".