Association between thyroid stimulating hormone levels and papillary thyroid cancer risk: A meta-analysis
Bibliographic record
Abstract
Abstract High thyroid stimulating hormone (TSH) levels may stimulate papillary thyroid cancer (PTC) cell proliferation; however, the relationship between TSH levels and PTC risk remains controversial. We aim to ascertain the association through a meta-analysis. Literature searches were conducted in PubMed, Embase, and Web of Science databases. After literature screening, the methodological quality was assessed using the Newcastle-Ottawa Scale and Agency for Healthcare Research and Quality methods. Cochran’s Q and I 2 tests were used to evaluate heterogeneity in the meta-analysis. Egger’s test was applied to assess publication bias. A total of 12 eligible studies were included in this meta-analysis; all were of moderate and high methodological quality. The pooled results suggested that increased TSH levels were significantly associated with PTC risk; however, the included studies were significantly heterogeneous. Stratification analysis indicated that the heterogeneity might be from the area or type of control. Although significant publication bias existed among the studies, the trim-and-fill method and sensitivity analysis revealed that the combined results were stable and robust. TSH levels are significantly associated with the PTC risk; however, more high-quality studies in large sample sizes are recommended to verify the extrapolation of these findings.
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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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.060 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".