XRCC1 and hOGG1 polymorphisms and endometrial carcinoma: A meta-analysis
Post-publication record
Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement; it reports them as false, which reads as “fine”.
Bibliographic record
Abstract
Endometrial carcinoma's (EC) etiology is complex and involves DNA repair gene polymorphisms like XRCC1-Arg399Gln and hOGG1-Ser326Cys, but their association with the disease is unclear. Following PRISMA, we conducted a systematic review and meta-analysis, collecting data from four databases. The studies needed to be population-based case-control studies examining the association between the named polymorphisms and EC. Quality was assessed with the Newcastle-Ottawa Scale. Pooled odds ratios (OR) and 95% confidence intervals (CI) were calculated, and subgroup analyses were conducted based on ethnicity. Seven studies were included. Both polymorphisms were found to significantly increase EC risk, particularly in Caucasians. XRCC1-Arg399Gln showed a dominant model OR of 1.14 (95% CI: 1.01-1.29) and a homozygous model OR of 1.59 (95% CI: 1.12-2.25). The heterozygote model OR for hOGG1-Ser326Cys was 1.29 (95% CI: 1.02-1.63), and the allele OR was 1.31 (95% CI: 1.07-1.60). XRCC1-Arg399Gln and hOGG1-Ser326Cys may increase EC risk, primarily in Caucasian women, emphasizing the role of DNA repair in disease susceptibility. More extensive studies are needed to validate these findings in diverse ethnicities and investigate other DNA repair gene polymorphisms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.040 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".