Recovery from a Spinal Cord Injury: Significance of Compensation, Neural Plasticity, and Repair
Bibliographic record
Abstract
Clinical recovery after a lesion of the central nervous system (CNS) can be attributed to mechanisms of functional compensation, neural plasticity, and/or repair. The relative impact of each of these mechanisms after a human spinal cord injury (SCI) has been explored in a prospective European multi-center study in 460 acute traumatic SCI subjects. Functional (activities of daily living and ambulatory capacity), neurological (sensory-motor deficits), and spinal conductivity (motor- and somato-sensory evoked potentials) measures were repeatedly followed over 12 months. In accordance with previous studies, complete SCI subjects (cSCI; n = 217) improved in activities of daily living unrelated to changes of the neurological condition, while incomplete SCI subjects (iSCI; n = 243) showed a greater functional and neurological recovery. The functional recovery in iSCI subjects was not related to an improvement of spinal conductivity, as reflected in unchanged latencies of the evoked potentials. This is in line with animal studies, where spinal conductivity of damaged spinal tracts has been reported to remain unchanged. These findings support the assumption that functional recovery occurs by compensation, especially in cSCI and by neural plasticity leading to a greater improvement in iSCI. Relevant repair of damaged spinal pathways does not take place.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".