The Impact of Exercise Training Interventions on Flow-Mediated Dilation: An Umbrella Review Protocol
Bibliographic record
Abstract
Introduction. (Cardio)vascular diseases are among the top causes of death in western societies. The impact of exercise training interventions to improve endothelial-dependent, flow-mediated dilation (FMD) responses has been reviewed extensively. These reviews may differ in their inclusion criteria, exercise type, exercise mode, exercise intensity, specific research questions, and conclusions. Comparing and contrasting these reviews will assist with the determination of optimal exercise programs across healthy and clinical populations.Objectives. We will conduct an umbrella review (or review of reviews) on systematic reviews and meta-analyses that examined the impact of exercise training interventions on peripheral artery FMD. The impact of exercise training design, population or artery studied, FMD methodology, and quality of reviews will be explored.Methods. A database search will be conducted in Scopus, EMBASE, MEDLINE, CINAHL, and Academic Search Premier for systematic reviews and meta-analyses on exercise training and FMD. All reviews must be conducted in adults (≥18 years). No limitation will be placed on the population (disease status, sex, etc.) or type of exercise training. Study quality will be determined using the Joanna Briggs Institute critical appraisal checklist for systematic reviews. Two independent screeners will examine titles, abstracts, full texts of relevant sources, and conduct the quality assessments. The results will be presented narratively and in a tabular format to align with the review objectives.Conclusion. This umbrella review may provide insight into the optimal training program to improve arterial health and act as an agent of change for modifying existing community exercise programs or clinical rehabilitation programs.
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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 | 0.098 | 0.102 |
| Meta-epidemiology (narrow) | 0.007 | 0.007 |
| Meta-epidemiology (broad) | 0.015 | 0.015 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.011 | 0.009 |
| Insufficient payload (model declined to judge) | 0.104 | 0.023 |
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".