Diet quality during preconception or pregnancy and gestational weight gain: protocol for a systematic review and meta-analysis
Bibliographic record
Abstract
Introduction Inappropriate gestational weight gain (GWG), including inadequate and excessive GWG, has become pandemic across nations and continents. This review aims to synthesise the evidence on the correlation between diet quality and GWG. If this association is confirmed, improving diet quality could become an intervention target in the efforts to reduce inappropriate GWG. Methods and analysis We will conduct a systematic review of all prospective cohort studies on diet quality in preconception or pregnancy and GWG. Our secondary outcomes include gestational diabetes, pre-eclampsia and birth weight. A comprehensive search of all published articles in MEDLINE ALL (Ovid), Embase (Ovid), Food Science and Technology Abstracts (Ovid) and CINAHL (EBSCOHost), from database creation to 20 April 2019, will be conducted. Studies will be screened for eligibility by title, abstract and full text in duplicate by two independent reviewers. Study quality and risk of bias will be assessed using the adapted Newcastle–Ottawa Scale. Results will be reported following the meta-analysis of observational studies in epidemiology guidelines. If sufficient data are available, a meta-analysis will be conducted to synthesise the effect size reported as OR with 95% CI using both fixed-effect and random-effect models. I 2 statistics and visual inspection of the forest plots will be used to assess heterogeneity and identify the potential sources of heterogeneity. Publication bias will be assessed by visual inspections of funnel plots and Egger’s test. Ethics and dissemination Formal ethical approval is not required as no primary data will be collected. We aim to publish the results of this study in a peer-reviewed journal and present them at conferences and scientific meetings to promote knowledge transfer. PROSPERO registration number CRD42019128732
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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 | 0.084 | 0.103 |
| Meta-epidemiology (narrow) | 0.007 | 0.005 |
| Meta-epidemiology (broad) | 0.022 | 0.029 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.056 | 0.007 |
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".