Diet and exercise interventions for individuals at risk for type 2 diabetes: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: Global rates of type 2 diabetes (T2D) are on the rise and there is a need for both effective and replicable interventions to decrease this incidence. Systematic reviews highlight the efficacy of diet and exercise interventions in decreasing T2D risk; however, no review to date provides clear information regarding intervention details (eg, what is delivered, by whom, to whom, when, and mode of delivery). This paper outlines the protocol for a scoping review summarising intervention characteristics of diet and exercise programmes for individuals at risk for T2D. From the included studies and through the use of the Template for Intervention Description and Replication (TIDieR), the scoping review that results from this protocol paper will provide a narrative analysis of how diabetes prevention programmes are being reported and implemented. METHODS: A comprehensive search strategy is outlined to identify studies within Medline, CINAHL, PsycINFO, EMBASE and SPORTDiscus. The search strategy will include terms relating to diet and exercise interventions and diabetes risk. To determine eligible studies, predefined inclusion and exclusion criteria will be used independently by two review authors. To be included, studies must be delivering a diet and/or exercise intervention among adults who have been identified as at risk for developing T2D with an outcome related to diabetes prevention. Data extraction of those studies that meet inclusion criteria will be guided by the TIDieR). ETHICS AND DISSEMINATION: Ethical approval is not required as this review will be using previously collected data. Review findings will be presented at scientific conferences and published in a peer-reviewed journal.
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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 | 0.086 | 0.074 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.118 | 0.029 |
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".