Effect of tree nuts on glycemic control in diabetes: a systematic review and meta‐analysis of randomized controlled dietary trials (1025.16)
Bibliographic record
Abstract
Background: Tree nut consumption is associated with reduced diabetes risk, however, results from randomized trials on glycemic control have been inconsistent. Aim: We conducted a systematic review and meta‐analysis of randomized controlled trials to assess the effect of tree nuts on glycemic control in individuals with diabetes. Methods: We searched MEDLINE, EMBASE, CINAHL, and Cochrane databases through 14 May 2013 for relevant randomized trials 蠅3‐weeks reporting HbA1c, fasting glucose, fasting insulin, and/or HOMA‐IR. Two independent reviewers extracted relevant data. Data were pooled using the generic inverse variance method and expressed as mean differences (MD) with 95% confidence intervals (CI). Heterogeneity was assessed by Cochran’s Q and quantified by I2. Results: 10 trials (n=374) met the eligibility criteria. Diets emphasizing tree nuts significantly lowered HbA1c (MD=‐0.11 %, 95% CI:‐0.18, ‐0.04 %; P=0.001) and fasting glucose (MD=‐0.20 mmol/L, 95% CI:‐0.38, ‐0.03 mmol/L; P=0.02) compared with isocaloric control diets. No significant treatment effects were observed for fasting insulin and HOMA‐IR. Limitations: Majority of trials were of poor quality and short duration. Conclusion: Pooled analyses show diets high in tree nuts improve glycemic control in individuals with type 2 diabetes. Longer, higher quality trials are needed. Clinicaltrials.gov identifier: NCT01630980 Grant Funding Source : International Tree Nut Council Nutrition Research & Education Foundation
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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 | 0.019 | 0.046 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.037 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".