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Record W2100771859 · doi:10.1517/13543784.2015.1009629

Investigational drugs for the treatment of spinal cord injury: review of preclinical studies and evaluation of clinical trials from Phase I to II

2015· review· en· W2100771859 on OpenAlexafffund
Narihito Nagoshi, Michael G. Fehlings

Bibliographic record

VenueExpert Opinion on Investigational Drugs · 2015
Typereview
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsUniversity of TorontoToronto Western HospitalUniversity Health Network
FundersAOSpineOntario Neurotrauma FoundationEuropean CommissionAcorda TherapeuticsHadassah Medical OrganizationMedtronic
KeywordsMedicineClinical trialRiluzoleSpinal cord injuryTolerabilityIntensive care medicineNeuroprotectionPharmacologyGabapentinAdverse effectSpinal cordDiseaseInternal medicineAmyotrophic lateral sclerosisPathologyAlternative medicine

Abstract

fetched live from OpenAlex

INTRODUCTION: Efforts in basic research have clarified mechanisms involved in spinal cord injury (SCI), and resulted in positive findings using experimental treatments including cell transplantation and drug administration preclinically. Based on accumulated results, various clinical trials have begun for human SCI. AREAS COVERED: In this review, the authors focus on five investigational drugs: riluzole, minocycline, Rho protein antagonist, magnesium chloride in polyethylene glycol formulation, and basic fibroblast growth factor. All drugs have established safety and tolerability from Phase I clinical trials, and are now in Phase II. They have been proven to have neuroprotective and/or neuroregenerative effects in animal models of SCI. EXPERT OPINION: To date, diverse drugs have been translated into clinical trials, but none have reached clinical application. A key gap was the lack of reliable biomarkers for SCI to fast-track Phase I/II trials. Furthermore, problems were often due to lack of adequate outcome assessments for both animal models and SCI patients. In order to advance clinical trials more quickly and with greater success, more clinically relevant animal models should be used in basic research. Clinically, it is indispensable to use appropriate outcome measurements and to construct a wide network among clinical centers to validate the efficacy of drugs.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.022
metaresearch head score (Gemma)0.044
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.946
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0220.044
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0060.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.799
GPT teacher head0.706
Teacher spread0.093 · 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 teacher head, not a consensus.

Study designOther design
Domainnot available
GenreReview

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

Citations49
Published2015
Admission routes2
Has abstractyes

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