Is Cardiac Rehabilitation Exercise Feasible for People with Mild Cognitive Impairment?
Bibliographic record
Abstract
BACKGROUND: Exercise is a promising strategy to prevent dementia, but no clinically supervised exercise program is widely available to people with mild cognitive impairment (MCI). The objective was to survey health professionals to assess the feasibility of using cardiac rehabilitation exercise programs for MCI populations. METHODS: We distributed surveys to: 1) health professionals working in cardiac rehabilitation exercise programs (36/72 responded); and 2) physicians who treat MCI (22/32 responded). Questions addressed clinician and clinic characteristics and feasibility of referring and accommodating people with MCI. RESULTS: Most cardiac rehabilitation exercise programs currently treat people with MCI (61.1%). Nearly all were willing and able to accept people with MCI and comorbid vascular risk (91.7%), though only a minority could accept MCI without vascular risk (16.7%). Although most physicians recommend exercise to people with MCI (63.6%), few referred patients with MCI to programs or people to guide exercise (27.3%). However, all physicians (100%) would refer patients with MCI to a cardiac rehabilitation exercise program. CONCLUSIONS: Our study supports cardiac rehabilitation exercise programs as a feasible model of exercise for patients with MCI with vascular risk. Patients with and without vascular risk could likely be accommodated if program mandates were expanded.
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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 | 0.003 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".