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Record W4402895505 · doi:10.3389/ebm.2024.10266

Bridging the gap: a translational perspective in spinal cord injury

2024· review· en· W4402895505 on OpenAlexaff
Omar Imad Hassan, Soichiro Takamiya, Azam Asgarihafshejani, Michael G. Fehlings

Bibliographic record

VenueExperimental Biology and Medicine · 2024
Typereview
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsToronto Western HospitalUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsSpinal cord injuryMedicineClinical trialTranslational researchIntensive care medicineNeurosciencePsychologySpinal cordPsychiatryPathology

Abstract

fetched live from OpenAlex

Traumatic spinal cord injury (SCI) is a devastating and complex condition to treat with no curative options. In the past few decades, rapid advancements in our understanding of SCI pathophysiology as well as the mergence of new treatments has created more optimism. Focusing on clinical translation, this paper provides a comprehensive overview of SCI through its epidemiology, pathophysiology, currently employed management strategies, and emerging therapeutic approaches. Additionally, it emphasizes the importance of addressing the heavy quality of life (QoL) challenges faced by SCI patients and their desires, providing a basis to tailor patient-centric forms of care. Furthermore, this paper discusses the frequently encountered barriers in translation from preclinical models to clinical settings. It also seeks to summarize significant completed and ongoing SCI clinical trials focused on neuroprotective and neuroregenerative strategies. While developing a cohesive regenerative treatment strategy remains challenging, even modest improvements in sensory and motor function can offer meaningful benefits and motivation for patients coping with this highly debilitating condition.

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.979
Threshold uncertainty score0.767

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.149
GPT teacher head0.547
Teacher spread0.398 · 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 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

Citations11
Published2024
Admission routes1
Has abstractyes

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