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Record W4396935242 · doi:10.3389/fnins.2024.1372920

Advancements in neuroregenerative and neuroprotective therapies for traumatic spinal cord injury

2024· article· en· W4396935242 on OpenAlexaff
Gregor Fischer, Linda Bättig, Martin N. Stienen, Armin Curt, Michael G. Fehlings, Nader Hejrati

Bibliographic record

VenueFrontiers in Neuroscience · 2024
Typearticle
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMedicineNeuroprotectionSpinal cord injuryQuality of life (healthcare)Psychological interventionClinical trialIntensive care medicineRehabilitationNeuroscienceSpinal cordPhysical therapyPsychologyPathologyPsychiatryPharmacologyNursing

Abstract

fetched live from OpenAlex

Traumatic spinal cord injuries (SCIs) continue to be a major healthcare concern, with a rising prevalence worldwide. In response to this growing medical challenge, considerable scientific attention has been devoted to developing neuroprotective and neuroregenerative strategies aimed at improving the prognosis and quality of life for individuals with SCIs. This comprehensive review aims to provide an up-to-date and thorough overview of the latest neuroregenerative and neuroprotective therapies currently under investigation. These strategies encompass a multifaceted approach that include neuropharmacological interventions, cell-based therapies, and other promising strategies such as biomaterial scaffolds and neuro-modulation therapies. In addition, the review discusses the importance of acute clinical management, including the role of hemodynamic management as well as timing and technical aspects of surgery as key factors mitigating the secondary injury following SCI. In conclusion, this review underscores the ongoing scientific efforts to enhance patient outcomes and quality of life, focusing on upcoming strategies for the management of traumatic SCI. Each section provides a working knowledge of the fundamental preclinical and patient trials relevant to clinicians while underscoring the pathophysiologic rationale for the therapies.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.655
Threshold uncertainty score0.552

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
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 head0.063
GPT teacher head0.408
Teacher spread0.345 · 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.

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

Citations16
Published2024
Admission routes1
Has abstractyes

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