Emerging advances in spinal cord injury: An introductory overview
Bibliographic record
Abstract
Spinal cord injury (SCI) remains a devastating condition with limited treatment options. This Focus Issue highlights recent clinical and translational advances across the continuum of SCI care from prehospital coordination to novel therapeutics. The articles cover multidisciplinary strategies during the early post-injury phase, including acute pre-hospital care and pragmatic alternatives for settings with limited medical resources. A historical perspective on the evolution of surgical timing and a comprehensive reappraisal of the pathophysiology of central cord syndrome provide valuable clinical context. Emerging diagnostic innovations are prominently featured, including novel biomarkers such as cerebrospinal fluid pressure dynamics, quantitative MRI, and navigated transcranial magnetic stimulation, which offer the potential to refine diagnosis and personalize prognostication. In parallel, emerging therapeutic technologies such as 3D-printed surgical guides and clinical applications of stem cell therapies represent significant advances in addressing long-standing challenges. The issue also includes comprehensive reviews of current classification systems and diagnostic criteria for both spine fractures and degenerative cervical myelopathy. Together, these contributions provide a multidisciplinary overview of the evolving landscape in SCI research and care, bridging basic science with clinical practice across diverse healthcare settings.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".