Text2PreventCVD: protocol for a systematic review and individual participant data meta-analysis of text message-based interventions for the prevention of cardiovascular diseases
Bibliographic record
Abstract
INTRODUCTION: Text message interventions have been shown to be effective in prevention and management of several non-communicable disease risk factors. However, the extent to which their effects might vary in different participants and settings is uncertain. We aim to conduct a systematic review and individual participant data (IPD) meta-analysis of randomised clinical trials examining text message interventions aimed to prevent cardiovascular diseases (CVD) through modification of cardiovascular risk factors (CVRFs). METHODS AND ANALYSIS: Systematic review and IPD meta-analysis will be conducted according to Preferred Reporting Items for Systematic review and Meta-Analysis of IPD (PRISMA-IPD) guidelines. Electronic database of published studies (MEDLINE, EMBASE, PsycINFO and Cochrane Library) and international trial registries will be searched to identify relevant randomised clinical trials. Authors of studies meeting the inclusion criteria will be invited to join the IPD meta-analysis group and contribute study data to the common database. The primary outcome will be the difference between intervention and control groups in blood pressure at 6-month follow-up. Key secondary outcomes include effects on lipid parameters, body mass index, smoking levels and self-reported quality of life. If sufficient data is available, we will also analyse blood pressure and other secondary outcomes at 12 months. IPD meta-analysis will be performed using a one-step approach and modelling data simultaneously while accounting for the clustering of the participants within studies. This study will use the existing data to assess the effectiveness of text message-based interventions on CVRFs, the consistency of any effects by participant subgroups and across different healthcare settings. ETHICS AND DISSEMINATION: Ethical approval was obtained for the individual studies by the trial investigators from relevant local ethics committees. This study will include anonymised data for secondary analysis and investigators will be asked to check that this is consistent with their existing approvals. Results will be disseminated via scientific forums including peer-reviewed publications and presentations at international conferences. TRIAL REGISTRATION NUMBER: CRD42016033236.
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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 | 0.073 | 0.118 |
| Meta-epidemiology (narrow) | 0.008 | 0.007 |
| Meta-epidemiology (broad) | 0.023 | 0.029 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.132 | 0.013 |
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".