Exposure to persistent organic pollutants and thyroid cancer risk: a study protocol of systematic review and meta-analysis
Bibliographic record
Abstract
Introduction The thyroid cancer incidence has been increasing all over the world. However, the aetiology of thyroid cancer remains unclear. A growing body of evidence suggested exposure to persistent organic pollutants (POPs) may play a role in the initiation of thyroid cancer, but the results are generally inconsistent across studies. This review aims to synthesise the evidence for the health effects of POPs on the risk of thyroid cancer. Methods and analysis This protocol was reported in accordance to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols (PRISMA) statements. A comprehensive search, including electronic database search (eg, PubMed, Embase, ProQuest and CNKI), website search and manual search, will be performed to identify all eligible studies. The Population, Exposure, Comparator and Outcome framework was used to clarify the inclusion and exclusion criteria. The Newcastle-Ottawa Scale will be used to assess the quality of included studies. Maximally adjusted effect estimates from individual studies will be summarised with random-effect models in a conservative manner.I2statistics and Q-tests will be used to test the heterogeneity across studies. We will perform extensive sensitivity analyses, such as confounding risk ratio (confounding), E-value, fixed-effect models, excluding the most relatively weighted study, including only the high-quality studies and many predesigned subgroup analyses, etc. The findings will be reported in accordance to the PRISMA guidelines. Ethics and dissemination Ethical approval is not required in this systematic review of published literatures. The results will be published in a peer-reviewed journal and presented at relevant conferences. PROSPERO registration number CRD42020181343.
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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 | 0.104 | 0.141 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.021 | 0.030 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.040 | 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".