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Record W4414462260 · doi:10.1093/brain/awaf354

Enhanced clinical trial stratification in spinal cord injury: the value of electrophysiology

2025· article· en· W4414462260 on OpenAlexafffund
Paulina S. Scheuren, Martin Schubert, Michèle Hubli, Catherine R. Jutzeler, R. Rupp, Rainer Abel, Doris Maier, Klaus F. Röhl, Michael Baumberger, Margret Hund‐Georgiadis, Marion Saur, Jesús Benito-Penalva, Kerstin Rehahn, Mirko Aach, Andreas Badke, Jiří Kříž, Patrick Freund, Norbert Weidner, Martin E. Schwab, John L. K. Kramer, Armin Curt

Bibliographic record

VenueBrain · 2025
Typearticle
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsInternational Collaboration On Repair DiscoveriesUniversity of British Columbia
FundersCanadian Institutes of Health ResearchEidgenössische Technische Hochschule ZürichStaatssekretariat für Bildung, Forschung und InnovationSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungSwiss Paraplegic FoundationInternational Foundation for Research in ParaplegiaUniversität ZürichEuropean CommissionMichael Smith Health Research BCNational Science Foundation
KeywordsElectrophysiologySpinal cordSomatosensory evoked potentialPlaceboClinical trialSpinal cord injuryCentral nervous system diseaseEvoked potential

Abstract

fetched live from OpenAlex

There are no approved interventional therapies, aside from neurorehabilitation, that enhance neurological recovery after acute traumatic spinal cord injury. A key challenge is the lack of biomarkers surpassing clinical standards for optimal stratification. We evaluated electrophysiological markers of preserved neuronal function to improve enrichment strategies over clinical measures. We hypothesized that participants with preserved electrophysiological markers would achieve greater neurological and functional recovery in response to a plasticity-inducing intervention. We conducted a retrospective stratification analysis of data from the recently completed randomized, placebo-controlled, phase 2b Nogo Inhibition in spinal cord injury (NISCI) trial (NCT03935321) investigating the efficacy of NG101, a recombinant human antibody that neutralizes the neurite outgrowth-inhibiting protein Nogo-A. Participants aged 18-70 years with acute (4-28 days) cervical spinal cord injury were eligible. At screening, all participants underwent clinical neurological examination and electrophysiological recordings (i.e. somatosensory evoked potentials). Treatment effect sizes for the recovery of upper extremity motor scores and spinal cord independence measure of self-care (6-month change) between NG101 and placebo groups were compared for stratification based on clinical versus electrophysiological criteria. Power analyses were conducted to estimate the required sample sizes needed for each method. The cohort included 116 participants (45.5 ± 16.8 years old, 74 NG101 and 41 placebo). Clinical stratification showed greater functional recovery in motor-incomplete participants treated with NG101 versus placebo [estimate 0.02 (95% confidence interval: 0.006-0.038), P = 0.007]. Electrophysiological stratification revealed greater functional recovery in participants with preserved somatosensory evoked potentials treated with NG101 versus placebo [0.04 (0.015-0.054), P < 0.001]. Effect sizes were large for electrophysiological stratification (Cohen's d = 0.94) but small for clinical stratification (Cohen's d = 0.46). Power analyses demonstrated smaller required sample sizes for electrophysiological stratification (required n = 32) versus clinical stratification (required n = 120). This study shows the value of electrophysiology in comparison to clinical measures for biomarker-driven enrichment and improved power in acute spinal cord injury trials. We emphasize the importance of functionally spared neuronal pathways in promoting recovery in response to plasticity-inducing interventions, such as anti-Nogo-A antibodies.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.340
metaresearch head score (Gemma)0.384
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.340
Threshold uncertainty score0.814

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.3400.384
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0050.005
Bibliometrics0.0020.002
Science and technology studies0.0010.003
Scholarly communication0.0040.006
Open science0.0020.002
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.090
GPT teacher head0.510
Teacher spread0.419 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2025
Admission routes2
Has abstractyes

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