Bibliographic record
Abstract
To evaluate and summarize the evidence of diet and physical activity management in patients with metabolic syndrome (MS). BMJ Best Practice, UpToDate, Joanna Briggs Institute (JBI) database, Agency for Healthcare Research and Quality (AHRQ) network, National Institute for Health and Clinical Excellence (NICE) network, Scottish Intercollegiate Guidelines Network (SIGN), Guidelines International Network (GIN), Medlive, Registered Nurses' Association of Ontario (RNAO) network, American Diabetes Association (ADA) network, New Zealand Guideline Group (NZGG) network, Canadian medical association clinical practice guidelines network, PubMed, EmBase, Web of Science, CINAHL, Cochrane Library, CNKI, China Science and Technology Journal Database, Wanfang Knowledge Data Service Platform and Chinese biomedical database were searched systematically to obtain guidelines, evidence summary, expert consensus, best practice information book, clinical decision-making, recommended practice, and systematic review on diet and physical activity management in patients with MS. The retrieval period is from the establishment of database to November 2021. Two researchers with evidence-based medicine background evaluated the quality and evidence level of the included literature. A total of 36 articles met the criteria, including 3 guidelines, 5 expert consensus, 1 clinical decision and 27 systematic reviews. We summarized 49 pieces of evidence related to diet and physical activity in patients with MS, involving 15 aspects, namely diet goals, diet patterns, diet time, carbohydrate intake, fat intake, fiber intake, salt intake, fruits, vegetables and grains intake, coffee intake, effects of diet, principle of physical activity, intensity, form, time of physical activity, effects of physical activity, physical activity prescription of patients with MS and cardiovascular disease, and the joint effects of diet and physical activity. Diet and physical activity management can effectively improve the health outcomes of patients with MS. Health professionals should choose and apply the best evidence with consideration of the clinical situation and patient preference.
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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 | 0.016 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.006 |
| Bibliometrics | 0.024 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".