A culturally tailored personaliseD nutrition intErvention in South ASIan women at risk of Gestational Diabetes Mellitus (DESI-GDM): a randomised controlled trial protocol
Bibliographic record
Abstract
INTRODUCTION: South Asians are more likely to develop gestational diabetes mellitus (GDM) than white Europeans. Diet and lifestyle modifications may prevent GDM and reduce undesirable outcomes in both the mother and offspring. Our study seeks to evaluate the effectiveness and participant acceptability of a culturally tailored, personalised nutrition intervention on the glucose area under the curve (AUC) after a 2-hour 75 g oral glucose tolerance test (OGTT) in pregnant women of South Asian ancestry with GDM risk factors. METHODS AND ANALYSIS: A total of 190 South Asian pregnant women with at least 2 of the following GDM risk factors-prepregnancy body mass index>23, age>29, poor-quality diet, family history of type 2 diabetes in a first-degree relative or GDM in a previous pregnancy will be enrolled during gestational weeks 12-18, and randomly assigned in a 1:1 ratio to: (1) usual care, plus weekly text messages to encourage walking and paper handouts or (2) a personalised nutrition plan developed and delivered by a culturally congruent dietitian and health coach; and FitBit to track steps. The intervention lasts 6-16 weeks, depending on week of recruitment. The primary outcome is the glucose AUC from a three-sample 75 g OGTT 24-28 weeks' gestation. The secondary outcome is GDM diagnosis, based on Born-in-Bradford criteria (fasting glucose>5.2 mmol/L or 2 hours post load>7.2 mmol/L). ETHICS AND DISSEMINATION: The study has been approved by the Hamilton Integrated Research Ethics Board (HiREB #10942). Findings will be disseminated among academics and policy-makers through scientific publications along with community-orientated strategies. TRIAL REGISTRATION NUMBER: NCT03607799.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.046 | 0.004 |
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".