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Record W4376878742 · doi:10.1016/j.jor.2023.05.007

Current insights into the management of spinal cord injury

2023· article· en· W4376878742 on OpenAlexaff
Nisaharan Srikandarajah, Mohammed Ali Alvi, Michael G. Fehlings

Bibliographic record

VenueJournal of Orthopaedics · 2023
Typearticle
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsUniversity Health NetworkUniversity of Toronto
Fundersnot available
KeywordsMedicineSpinal cord injuryNeuromodulationRehabilitationSpinal cordQuality of life (healthcare)Physical medicine and rehabilitationNarrative reviewPhysical therapyIntensive care medicineCentral nervous systemInternal medicine

Abstract

fetched live from OpenAlex

Background: Traumatic spinal cord injury (SCI) is a serious disorder that results in severe impairment of neurological function as well as disability, ultimately reducing a patient's quality of life. The pathophysiology of SCI involves a primary and secondary phase, which causes neurological injury. Methods: Narrative review on current clinical management of spinal cord injury and emerging therapies. Results: This review explores the management of SCI through early decompressive surgery, optimizing mean arterial pressure, steroid therapy and focused rehabilitation. These management strategies reduce secondary injury mechanisms to prevent the propagation of further neurological damage. The literature regarding emerging research is also explored in cell-based, gene, pharmacological and neuromodulation therapies, which aim to repair the spinal cord following the primary injury mechanism. Conclusions: Outcomes for patients with SCI can be enhanced and improved if primary and secondary phases of SCI can be addressed.

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)0.003
metaresearch head score (Gemma)0.012
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 score0.006
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0010.003
Scholarly communication0.0050.004
Open science0.0020.001
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0050.001

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 head0.065
GPT teacher head0.422
Teacher spread0.356 · 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

Citations15
Published2023
Admission routes1
Has abstractno

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