Birth Weight and Maternal Pre-Pregnancy Body Mass Index and the Risk of Childhood Type I Diabetes: A Systematic Review and Dose-Response Meta-Analysis of Cohort Studies of over Ten-Million Participants
Bibliographic record
Abstract
Search Strategy The recommendations of the Meta-Analysis of Observational Studies in Epidemiology guidelines (MOOSE) follow in the conduct of this systematic review and meta-analysis. A comprehensive literature search of retrospective and prospective cohort studies conduct in PubMed/MEDLINE, Scopus, Cochrane library, and Web of Science databases, a from database inception to now. The search strategy comprise of “body mass index” OR “Birth weight” AND “Type 1 diabetes” relevant terms. Reference lists from original and review papers investigate to identify additional relevant studies and an email alert service activate in databases to avoid any missing new articles published after our search. Inclusion criteria Studies include if they adhere following inclusion criteria: 1) investigated the association between BMI or birth weight and type I diabetes in a retrospective or prospective study; 2) reported appropriate estimates such as the hazard ratio (HR), risk ratio (RR), or odds ratio (OR) and the corresponding 95% confidence intervals (CI). Studies were excluded if they were conference papers, review papers, editorials, non-human studies, case reports, ecological studies, or letters without sufficient data. Multiple reports for same cohort, cross sectional, case-control, and intervention studies were also excluded. Data extraction and quality assessment Title and abstract screening of searched studies conduct by Two authors, independently. Subsequently, full texts studies assesse, and discrepancies resolve through discussion with a senior author. Relevant data extract according to extraction forms, where the following data extracte: the first authors, year of publication, year of start and finishing studies, study location, cohort name, number of participants, summary estimates and 95% CIs of type I diabetes. Fully adjusted models were used for the meta-analysis. The Newcastle-Ottawa Quality Assessment Scale (NOS) use to assessment of studies quality and Studies were considered with aggregate scores of 0-6 as having low quality and 6.5-9 as having high quality. Statistical analysis All statistical analyses conduct using STATA 14.0 statistical software (Stata Corporation, College Station, Texas, USA). In order to combine risk estimates of type I diabetes in children, DerSimonian and Laird random effects model used.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".