Aerobic Exercise Recommendations to Optimize Best Practices in Care After Stroke: AEROBICS 2019 Update
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Most stroke survivors have very low levels of cardiovascular fitness, which limits mobility and leads to further physical deconditioning, increased sedentary behavior, and heightened risk of recurrent stroke. Although clinical guidelines recommend that aerobic exercise be a part of routine stroke rehabilitation, clinical uptake has been suboptimal. In 2013, an international group of stroke rehabilitation experts developed a user-friendly set of recommendations to guide screening and prescription-the Aerobic Exercise Recommendations to Optimize Best Practices in Care after Stroke (AEROBICS 2013). The objective of this project was to update AEROBICS 2013 using the highest quality of evidence currently available. The first step was to conduct a comprehensive review of literature from 2012 to 2018 related to aerobic exercise poststroke. A working group of the original consensus panel members drafted revisions based on synthesis. An iterative process was used to achieve agreement among all panel members. Final revisions included: (1) addition of 115 new references to replace or augment those in the original AEROBICS document, (2) rewording of the original recommendations and supporting material, and (3) addition of 2 new recommendations regarding prescription. The quality of evidence from which these recommendations were derived ranged from low to high. The AEROBICS 2019 Update should make it easier for clinicians to screen for, and prescribe, aerobic exercise in stroke rehabilitation. Clinical implementation will not only help to narrow the gap between evidence and practice but also reduce current variability and uncertainty regarding the role of aerobic exercise in recovery after stroke.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it