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Record W2035120744 · doi:10.3171/foc.2008.25.11.e2

Current status of acute spinal cord injury pathophysiology and emerging therapies: promise on the horizon

2008· review· en· W2035120744 on OpenAlexaff
James W. Rowland, Gregory W. J. Hawryluk, Brian K. Kwon, Michael G. Fehlings

Bibliographic record

VenueNeurosurgical FOCUS · 2008
Typereview
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsUniversity Health NetworkUniversity of British ColumbiaToronto Western HospitalUniversity of Toronto
Fundersnot available
KeywordsMedicineSpinal cord injuryExcitotoxicityNeurosciencePathophysiologyNeuroprotectionSpinal cordPathologyBiologyPharmacologyInternal medicineGlutamate receptor

Abstract

fetched live from OpenAlex

This review summarizes the current understanding of spinal cord injury pathophysiology and discusses important emerging regenerative approaches that have been translated into clinical trials or have a strong potential to do so. The pathophysiology of spinal cord injury involves a primary mechanical injury that directly disrupts axons, blood vessels, and cell membranes. This primary mechanical injury is followed by a secondary injury phase involving vascular dysfunction, edema, ischemia, excitotoxicity, electrolyte shifts, free radical production, inflammation, and delayed apoptotic cell death. Following injury, the mammalian central nervous system fails to adequately regenerate due to intrinsic inhibitory factors expressed on central myelin and the extracellular matrix of the posttraumatic gliotic scar. Regenerative approaches to block inhibitory signals including Nogo and the Rho-Rho-associated kinase pathways have shown promise and are in early stages of clinical evaluation. Cell-based strategies including using neural stem cells to remyelinate spared axons are an attractive emerging approach.

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)0.002
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0070.004

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 head0.092
GPT teacher head0.433
Teacher spread0.341 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations826
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueNeurosurgical FOCUSSame topicSpinal Cord Injury ResearchFrench-language works237,207