Solid Tumor Incidence and Patterns in Individuals With Down Syndrome: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
BACKGROUND: Individuals with Down syndrome (DS) exhibit a distinct pattern of solid tumor occurrence. However, the incidence of specific solid tumors remains inconsistent. We aimed to assess the burden of solid tumors in individuals with DS and compare it to the general population. METHODS: We conducted a systematic literature search across PubMed, Scopus, Web of Science, and Google Scholar to identify cohort studies reporting the incidence of solid tumors in individuals with DS. Statistical analyses were performed using random-effects model, along with subgroup analyses, heterogeneity evaluation, publication bias, and sensitivity analyses. RESULTS: The systematic search identified 8049 records. Fourteen studies were included, representing 34,561 individuals with 452,409.72 person-years of follow-up. The pooled incidence rate of solid tumors in individuals with DS was 95 cases per 100,000 person-years (95% confidence interval [CI]: 42-170). Compared to the general population, individuals with DS exhibited a significantly lower incidence rate of solid tumors (standardized incidence ratio [SIR]: 0.69, 95% CI: 0.51-0.94), with significantly lower rates observed for female genital organ (SIR: 0.44, 95% CI: 0.20-0.99), breast (SIR: 0.35, 95% CI: 0.17-0.73), skin (SIR: 0.24, 95% CI: 0.11-0.50), and lung (SIR: 0.10, 95% CI: 0.02-0.62) cancers. In contrast, higher incidences were identified for testicular (SIR: 4.28, 95% CI: 2.61-7.03) and liver (SIR: 3.72, 95% CI: 1.09-12.75) tumors. CONCLUSIONS: This study provides a comprehensive overview of solid tumor epidemiology in individuals with DS, emphasizing the need for population-specific screening measures.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.025 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".