Factors Predicting Motor Recovery and Functional Outcome After Traumatic Central Cord Syndrome
Bibliographic record
Abstract
STUDY DESIGN: A prospectively maintained database-generated retrospective review and cross-sectional outcome analysis was performed at a single academic center. OBJECTIVES: To assess the improvement in ASIA motor score (AMS) and secondarily to assess generic health related quality of life (HRQoL) and functional status; correlating these with variables that may predict outcome. SUMMARY OF BACKGROUND DATA: Many variables are potential contributors to motor recovery, patient function, and outcome following cervical trauma. Studies often suffer from low power, short follow-up, heterogeneous cohorts, and use of outcome instruments that are neither valid nor psychometrically sound. METHODS: AMS were collected within 72 hours of the time of injury and again at follow-up by trained examiners. The SF-36 and FIM were administered to all patients at follow-up. RESULTS: AMS improved from a mean of 58.7 at injury to a mean of 92.3 at follow-up. Bowel and bladder continence was reported by 81% while independent ambulation was reported by 86%. Final AMS was positively correlated with the AMS at injury, formal education, and presence of spasticity at follow-up. Functional status (FIM) was positively correlated with higher AMS at injury, formal education, absence of comorbidities, absence of spasticity, and younger age. Generic HRQoL outcomes (SF-36) were improved in individuals with more formal education, fewer comorbidities, absence of spasticity, and anterior column fractures. CONCLUSIONS: Although the majority of patients improve to an AMS between 90 and 100, many have significant disability and are less functional than the general population. Significant predictive variables include the initial motor score, formal education, comorbidities, age at injury, and development of spasticity. An assessment of more than just the motor score is required to obtain an appreciation of the function and outcomes in this population.
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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) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".