Weight Loss After Stroke Through an Intensive Lifestyle Intervention (Group Lifestyle Balance-Cerebrovascular Accident): Protocol for a Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Weight gain can be a consequence of stroke, or cerebrovascular accident (CVA), because of impaired mobility, behavioral and emotional disorders, and sensory losses. Weight gain increases the patient's risk of recurrent stroke and chronic diseases, such as diabetes, metabolic syndrome, and pulmonary and heart disease. Approaches to weight loss in this population are lacking, although necessary because of the unique physiological and cognitive needs of persons after a stroke. Evidence shows that intensive behavioral therapy interventions that address both physical activity and diet offer the greatest potential for weight loss. The Group Lifestyle Balance (GLB) intervention is a 12-month, evidence-based weight loss program that has been used extensively with the general population; this program was modified to meet the needs of people who have had a stroke (GLB-CVA). OBJECTIVE: This randomized controlled trial (RCT) aims to examine the efficacy of the GLB-CVA on weight and secondary outcomes, compared with that of a waitlist control group. METHODS: This RCT will enroll and randomize 64 patients over an 18-month period. RESULTS: Currently, 51 people are waitlisted, with 23 out of 51 screened and 16 out of 23 eligible. CONCLUSIONS: It is anticipated that the findings from this RCT will contribute to the evidence base regarding weight loss strategies for people living with stroke. CLINICAL TRIAL: ClinicalTrials.gov NCT03873467; https://clinicaltrials.gov/ct2/show/NCT03873467.
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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 | 0.036 | 0.035 |
| Meta-epidemiology (narrow) | 0.007 | 0.004 |
| Meta-epidemiology (broad) | 0.015 | 0.008 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.085 | 0.015 |
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".