Bibliographic record
Abstract
How does exercise help recovering stroke patients?The after-effects of a stroke can severely alter a person's quality of life.Effects range from mobility issues to changes in brain function.However, the brain is surprisingly adaptable and often, with the right approaches, these lost functions can be partly or completely restored.At Western University in Canada, Dr Sue Peters, is investigating the role of exercise in improving post-stroke recovery, and has found some promising results. GlossaryGrowth factor -a biological molecule that affects the growth and division of cells Neuroimagingproducing images of the brain using bioimaging techniques Neuroplasticity -the ability of the nervous system to reorganise parts of itself, especially following injury Neurotransmitter -a chemical messenger secreted by a nerve cell that stimulates activity in another cell Physical therapy -a healthcare profession that focuses on improving the body's physical movements Physiology -the field of biology that focuses on how the bodies of living organisms work Rehabilitation -the process of returning to a good quality of life following a health issue Stroke -a medical incident that happens when blood supply to part of the brain is disrupted, leading to damage to that part of the brain
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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".