Fighting for Each Segment: Estimating the Clinical Value of Cervical and Thoracic Segments in SCI
Bibliographic record
Abstract
Patients suffering from complete spinal cord injury (SCI) are the most likely candidates for the application of new interventions for neural repair and regeneration. It is assumed that some of these treatments will have their strongest impact at the segmental level. Therefore, it is important to evaluate the clinical relevance of potential changes at the segmental levels concerning both improvement and deterioration. Data of 98 motor complete SCI patients were derived from the European Multicenter Study of Human Spinal Cord Injury database. Six months after injury, the ASIA motor score and Spinal Cord Independence Measure (SCIM) were assessed as dependent variables (linear regression analysis) to disclose the difference between each segment. Separate analyses using linear regression for tetraplegic patients (n = 39) and paraplegic patients with thoracic lesions (n = 54) were performed to calculate the difference between each spinal segment. In tetraplegic patients, both the ASIA motor score and the SCIM revealed relevant differences per spinal segment (9 and 4 points, respectively) while in paraplegic patients there was no difference for the SCIM and the ASIA motor score between T2 and T8. We suggest that in complete tetraplegic patients, changes of even one spinal segment will either improve or degrade both motor function and independence. Segmental changes at the thoracic level are not assessable by the ASIA motor score and SCIM tests. Therefore, the assessment of efficacy and safety in thoracic patients by these two tests has limited value when applied to cervical SCI. These findings may be considered in clinical trials for the evaluation of beneficial effects and risk management when treating patients with spinal cord injury.
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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 | 0.002 | 0.001 |
| 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 | 0.001 |
| 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".