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Record W3216852367 · doi:10.21037/tcr-21-1508

Association between single nucleotide polymorphisms of IL-6 and susceptibility to skin cancer: a meta-analysis and systematic review

2021· article· en· W3216852367 on OpenAlexaboutno aff
Keye Guo, Zhongming Lu, Xiaoping Wang, Jianjun Qiao

Bibliographic record

VenueTranslational Cancer Research · 2021
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmune Response and Inflammation
Canadian institutionsnot available
Fundersnot available
KeywordsMeta-analysisOdds ratioSingle-nucleotide polymorphismConfidence intervalInternal medicineMedicinePublication biasOncologyGastroenterologyGenotypeBiologyGeneticsGene

Abstract

fetched live from OpenAlex

BACKGROUND: gene polymorphisms and skin cancers. METHODS: Eligible studies were identified from searching PubMed, Embase, Web of Science and Cochrane. Pooled odds ratio (OR) and corresponding 95% confidence interval (CI) were obtained for the relationships between IL-6 174G/C and 597G/A polymorphisms and skin cancer using random-effects models. For the included studies, the Newcastle-Ottawa scale (NOS) score was calculated to assess study quality. Heterogeneity tests, sensitivity analysis, and publication bias assessments were also performed. Trim-and-fill method was used when publication bias existed aiming to adjusting OR. All data were analyzed in R (version 4.0.2). RESULTS: GG, OR =1.277, P=0.01), while there is no statistical significance in the subgroup of publication year after 2010 (P>0.05 for all comparisons). After publication bias adjustment, the results further suggested that 174G/C polymorphism is not associated with the risk of skin cancer. No elevated risk of skin cancer was found in the comparisons for 597G/A polymorphism. DISCUSSION: gene polymorphisms might not be associated with the susceptibility to skin cancer.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.364
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.101
GPT teacher head0.379
Teacher spread0.278 · 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 teacher head, not a consensus.

Study designBench or experimental
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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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