Aerobic Exercise Recommendations to Optimize Best Practices in Care After Stroke: AEROBICS 2019 Update
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.
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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 | 0.030 | 0.081 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.013 | 0.008 |
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".