Bibliographic record
Abstract
In the past, the spinal cord was considered a hard-wired network responsible for spinal reflexes and a conduit for long-range connections. This view has changed dramatically over the past few decades. It is now recognized as a plastic structure that has the potential to adapt to changing environments. While such changes occur under physiological conditions, the most dramatic alterations take place in response to pathological events. Many of the changes that occur following such pathological events are maladaptive, but some appear to help adapt to the new conditions. Although a number of studies have been devoted to elucidating the underlying mechanisms, in humans and animal models, the etiology and pathophysiology of various diseases impacting the spinal cord are still not well understood. In this review, we summarize current understanding and outstanding challenges for a number of diseases, including spinal muscular atrophy (SMA), amyotrophic laterals sclerosis (ALS), and spinal cord injury (SCI), with occasional relations to stroke. In particular, we focus on changes resulting from SCI (and stroke), and various influencing factors such as cause, site and extent of the afflicted damage.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".