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Record W6958243593 · doi:10.60692/ypbt8-va881

Association between IL-10 gene polymorphisms (− 1082 A/G, -819 T/C, -592 A/C) and hepatocellular carcinoma: a meta-analysis and trial sequential analysis

2023· article· en· W6958243593 on OpenAlexaboutno aff

Bibliographic record

VenueGreater South Information System · 2023
Typearticle
Languageen
FieldComputer Science
TopicMachine Learning and Data Classification
Canadian institutionsnot available
Fundersnot available
KeywordsHepatocellular carcinomaOdds ratioConfidence intervalGenetic modelAlleleMeta-analysisCarcinomaCarcinogenesis

Abstract

fetched live from OpenAlex

Abstract Background The carcinogenesis of hepatocellular carcinoma is complicated, and genetic factor may have the role in the malignant transformation of liver cells. IL-10 gene polymorphisms have been investigated for their potential roles in hepatocellular carcinoma This study aimed to investigate the relationship between polymorphisms of IL-10 (-1082 A/G, -819 T/C, -592 A/C), and hepatocellular carcinoma by performing a meta-analysis with eligible individual studies. Methods This study followed the PRISMA 2020 Checklist. Relevant studies were searched in health-related databases. The Newcastle-Ottawa Scale criteria were used to evaluate the studies quality. Pooled odds ratio (OR) and its 95% confidence interval (CI) were used to determine the strength of association between each polymorphism and hepatocellular carcinoma using five genetic models. Stratification was done by ethnic groups. Trial sequential analysis (TSA) was performed to determine the required information size. Results Fifteen case-control studies (n = 8182) were identified. Overall, the heterozygous model showed a marginal significant association only between IL-10 (-1082 A/G) and hepatocellular carcinoma risk (OR: 0.82, 95% CI: 0.67-1.00, 9 studies). On stratification, IL-10 (-1082 A/G) was significantly associated with hepatocellular carcinoma risk in the non-Asian population under dominant (OR: 0.62, 95% CI: 0.45–0.86, 4 studies), heterozygous (OR: 0.60, 95% CI: 0.43–0.85) and allelic models (OR: 0.79, 95% CI: 0.64–0.99). IL-10 (-819 T/C) was significantly associated with hepatocellular carcinoma risk only among non-Asians under the dominant (OR: 1.47, 95% CI: 1.02–2.13, 8 studies), recessive (OR: 1.99, 95% CI: 1.03–3.86, and homozygous models (OR: 2.18, 95% CI: 1.13–4.23). For IL-10 (-592 A/C) with 11 studies, there was no significant association with hepatocellular carcinoma in all five genetic models ( P values > 0.5). TSA plots indicated that the information size for firm evidence of effect was sufficient only for the analysis of IL-10 (-592 A/C), but not for the − 1082 A/G or -819 T/C. Conclusions Findings suggest that IL-10 (-1082 A/G and − 819 T/C) polymorphisms are associated with hepatocellular carcinoma in ethnic-specific manner. However, this evidence is not conclusive because the sample size was insufficient. IL-10 (-592 A/C) polymorphism was not associated with hepatocellular carcinoma albeit with sufficient information size. Future well-designed large case-control studies on IL-10 (-1082 A/G and − 819 T/C) with different ethnicities are recommended.

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 imitation

Not 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.

metaresearch head score (Codex)0.021
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.021
Threshold uncertainty score0.109

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.026
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0140.039
Bibliometrics0.0030.004
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.082
GPT teacher head0.250
Teacher spread0.168 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreEmpirical

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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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