Are early-onset spasms predictive of poor neurological recovery after traumatic spinal cord injury?
Bibliographic record
Abstract
CONTEXT: Following spinal cord injury (SCI), early spasms are associated with decreased functional recovery. It has also been hypothesized that early spasticity might sign underlying maladaptive neuroplasticity, which could translate in worse neurological outcomes. OBJECTIVE: In this context, this paper aims to evaluate if early-onset spasms are also associated with neurological outcomes after SCI. METHODS: A retrospective review of 196 cases from a prospective SCI database was conducted. The presence of early spasms during the acute hospitalization was assessed by a single physiatrist. The characteristics and long-term neurological outcomes of individuals with and without early spasms were first compared. Multivariate regression analyses were then performed to determine the relationship between early spasms and neurological outcomes. RESULTS: = 59) of patients presented early spasms. These patients had several distinguishing characteristics including higher odds of tetraplegia (vs. paraplegia) and more severe injuries. At the bivariate level, patients with early spasms had higher odds of improving at least 1 AIS grade between baseline and follow-up. However, this was not significant at the multivariate level. CONCLUSIONS: Early spasms are not significantly associated with poorer neurological outcomes, contrasting with the unwritten consensus that early spasticity translates maladaptive neuroplasticity.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".