Association of human Hedgehog interacting protein gene polymorphisms with the risk of chronic obstructive pulmonary disease: a meta-analysis
Bibliographic record
Abstract
To date, some studies revealed that HHIP gene polymorphisms may be associated with the risk of chronic obstructive pulmonary disease (COPD). Therefore, this meta-analysis explored the association between single-nucleotide polymorphisms (SNPs) of the human hedgehog interacting protein (HHIP) gene and susceptibility to COPD. Seven Chinese and English electronic databases were searched for eligible studies up to 30 May 2020. After the inclusion criteria were strictly followed, the Newcastle-Ottawa Scale (NOS) was used to evaluate the quality of the included studies. The pooled odds ratio (OR) of the 95% confidence interval (CI) under four different genetic models was calculated to evaluate the association strength between the SNPs and COPD. Egger’s test was used to evaluate publication bias. This meta-analysis was registered at PROSPERO (CRD42021235708). In total, 12 studies involving 6623 COPD patients and 11,373 healthy controls were included. Regarding rs13118928 and rs1828591, an A > G mutation increased the risk of COPD in Asian and Caucasian individuals, and the rs13147758 A > G mutation and rs10519717 C > T mutation increased the risk of COPD only in Asian people. No significant publication bias was observed. This meta-analysis provides a theoretical basis suggesting that HHIP gene polymorphisms may be associated with the risk of COPD.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.049 |
| Bibliometrics | 0.005 | 0.007 |
| 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) | 0.004 | 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".