Review: lifestyle education programmes lower glucose concentrations and reduce the incidence of type 2 diabetes
Bibliographic record
Abstract
Yamaoka K, Tango T. Efficacy of lifestyle education to prevent type 2 diabetes: a meta-analysis of randomized controlled trials. Diabetes Care 2005;28:2780–6.[OpenUrl][1][Abstract/FREE Full Text][2] Q Do lifestyle education programmes lower plasma glucose concentrations and reduce the incidence of type 2 diabetes in adults at high risk? ### ![Graphic][3] Data sources: Medline and Educational Resources Information Centre (ERIC) (1966 to November 2004). ### ![Graphic][4] Study selection and assessment: English language randomised controlled trials (RCTs) that compared a lifestyle education programme (diet with or without exercise interventions) with conventional education (usual exercise with or without general information about healthy food choices) in adults at high … [1]: {openurl}?query=rft.jtitle%253DDiabetes%2BCare%26rft.stitle%253DDiabetes%2BCare%26rft.aulast%253DYamaoka%26rft.auinit1%253DK.%26rft.volume%253D28%26rft.issue%253D11%26rft.spage%253D2780%26rft.epage%253D2786%26rft.atitle%253DEfficacy%2Bof%2BLifestyle%2BEducation%2Bto%2BPrevent%2BType%2B2%2BDiabetes%253A%2BA%2Bmeta-analysis%2Bof%2Brandomized%2Bcontrolled%2Btrials%26rft_id%253Dinfo%253Adoi%252F10.2337%252Fdiacare.28.11.2780%26rft_id%253Dinfo%253Apmid%252F16249558%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [2]: /lookup/ijlink?linkType=ABST&journalCode=diacare&resid=28/11/2780&atom=%2Febnurs%2F9%2F3%2F75.atom [3]: /embed/inline-graphic-1.gif [4]: /embed/inline-graphic-2.gif
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".