Student Competition (Knowledge Generation) ID 1985154
Bibliographic record
Abstract
Background Exercising after spinal cord injury (SCI) is necessary to prevent or reduce secondary complications such as obesity, cardiovascular disease, or type II diabetes. The effects of SCI on muscle and autonomic functions determine type, duration, and intensity of exercise capacity. Although SCI exercise guidelines exist, achieving these recommendations requires a comprehensive understanding of how muscle and autonomic function affect a person’s exercise capability. Therefore, we reviewed the effect of SCI level on muscle and sympathetic function during exercise for those living with SCI to better develop strategies to achieve these guidelines. Methods A literature review of exercise, muscle control and SCI was performed to identify muscles innervated by each level and how key sympathetic tissues and organs required for exercise are affected by SCI. We identified spinal levels responsible for increasing heart rate, cardiovascular smooth muscle contraction, inducing sweat and activating adrenal glands since these are essential in maintaining high intensity and long duration exercise. We translated the information into a comprehensive user-friendly poster. Results A comprehensive graphical poster was developed for those living with SCI to understand and identify how their level of SCI affects their muscle and sympathetic function needed for optimal exercise and to meet exercise guidelines. Conclusions This infographic fills a void since this type of ‘person-centred’ information is lacking in the SCI and exercise field. The knowledge acquired through this infographic could further guide training practices and exercise modifications to increase exercise capacity and quality of life for those living with SCI.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.941 | 0.820 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".