Dominant and recessive genetic models of LSP1 Gene rs3817198 Polymorphism and Breast Cancer Risk: A Systematic Review and Meta-analysis Study
Bibliographic record
Abstract
Abstract: The aim of this meta-analysis is to predict effect of dominant and recessive genetic models of LSP1 gene rs3817198 polymorphism and breast cancer risk. We performed the meta-analysis according to PRISMA protocol. Three databases including PubMed/Medline, Web of sciences and EMBASE was searched. We included all studies that evaluated association of LSP1 gene rs3817198 and breast cancer risk in this meta-analysis. ORs and their reported 95% confidence interval (CI) for dominant and recessive inheritance models were extracted form final retrieved studies. ORs were pooled using both fixed and the random-effect models. Egger’s test and contour-enhanced funnel plot was used to evaluate Publication bias and small study effect. Twelve publications were eligible for final analysis after applying of inclusion and exclusion criteria. Totally, this meta-analysis composed of 15,530 cases and 20,258 controls. This study revealed significant association between LSP1 gene rs3817198 polymorphism and breast cancer in dominant genetic model (OR=1.07 [1.01-1.14]). Inversely, no association was found in recessive genetic model (OR=1.10 [0.93-1.32]). Subgroup analysis displayed a significant association in population-based studies and European & American & African population only in dominant genetic model. Begg’s funnel plot and Egger’s test were used for assessment of publication bias and confirmed no publication bias. Finally, we think LSP1 gene rs3817198 polymorphism is associated with breast cancer risk and the risk is more prominent in Caucasians. Nevertheless, further studies are required to confirm the association.
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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 | 0.017 | 0.029 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.047 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| 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".