Environmental triggers of pediatric type 1 diabetes: A protocol for a systematic review and meta-analysis on the impact of COVID-19 infection and vaccination
Bibliographic record
Abstract
INTRODUCTION: The study aimed to systematically evaluate whether coronavirus disease (COVID-19) infection and/or vaccination act as environmental triggers for new-onset type 1 diabetes (T1D) in children and adolescents. MATERIAL AND METHODS: We will conduct a systematic review and meta-analysis in accordance with PRISMA 2020 guidelines. PubMed, Embase, Scopus, Web of Science, and Cochrane Library will be searched for studies from 2020 onward. Eligible studies must report new-onset T1D in individuals aged 0-19 years following confirmed SARS-CoV-2 infection or COVID-19 vaccination, with a clearly defined comparator group. Data will be extracted using the Population-Exposure-Comparator-Outcome (PECO) framework, aiming to capture diabetes incidence, diabetic ketoacidosis (DKA) severity, immunologic and genetic markers, direction of effect, and potential confounders. Risk of bias will be assessed with the Newcastle-Ottawa Scale, and GRADE will be used to assess certainty of evidence. A random-effects meta-analysis will be conducted if appropriate. EXPECTED RESULTS: We anticipate mapping the direction of effect (-, ¯, mixed, or = no difference) for each exposure-outcome pair, and identifying gaps related to immunologic markers, diagnostic timing, and confounding factors. CONCLUSIONS: This review will provide a comprehensive synthesis of evidence on infection- and vaccine-triggered pediatric T1D, informing future prospective studies and surveillance efforts.
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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 | 0.062 | 0.094 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.018 | 0.026 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.051 | 0.005 |
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".