Advanced neurological recovery translates into greater long-term functional independence after acute spinal cord injury
Bibliographic record
Abstract
ABSTRACT The absence of effective pharmacological interventions in acute traumatic spinal cord injury is a major problem in its management. A critical barrier in identifying such interventions lies in the vast heterogeneity of recovery profiles, which masks the potential efficacy of treatments in clinical trials. To determine the impact of temporal recovery profiles on long-term functional independence, we used EMSCI (European Multicenter Study about Spinal Cord Injury) data. Total motor scores from the International Standards for the Neurological Classification of Spinal Cord Injury (ISNCSCI) and the Spinal Cord Independence Measure (SCIM) were used to assess neurological and functional outcomes, respectively. We developed a classification method consisting of thresholding and unsupervised machine learning clustering and applied it to the total motor score profiles. Comparing SCIM scores between classes revealed that functional independence is significantly higher among patients displaying advanced neurological recovery profile. Our study suggests that the evaluation of temporal recovery profiles can provide novel insights in spinal cord injury clinical trials.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".