Rethinking Rehabilitation
Bibliographic record
Abstract
Although cardiac rehabilitation (CR) is safe and highly effective for individuals with various cardiovascular health conditions, to date there are only seven diagnoses or procedures identified by the Centers for Medicare & Medicaid Services that qualify for referral. When considering the growing number of individuals with cardiovascular disease (CVD), or other health conditions that increase the risk for CVD, it is important to determine the extent for which CR could benefit these populations. Furthermore, there are some patients who may currently be eligible for CR (spontaneous coronary artery dissection, left ventricular assistant device) but make up a relatively small proportion of the populations that are regularly attending and participating. Thus, these patient populations and special considerations for exercise might be less familiar to professionals who are supervising their programs. The purpose of this review is to summarize the current literature surrounding exercise testing and programming among four specific patient populations that either do not currently qualify for (chronic and end-stage renal disease, breast cancer survivor) or who are eligible but less commonly seen in CR (sudden coronary artery dissection, left ventricular assist device). While current evidence suggests that individuals with these health conditions can safely participate in and may benefit from supervised exercise programming, there is an immediate need for high-quality, multisite clinical trials to develop more specific exercise recommendations and support the inclusion of these populations in future CR programs.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".